diff --git a/notebooks/.ipynb_checkpoints/stacking-regression-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/stacking-regression-checkpoint.ipynb
index 8f78937..a366cd5 100644
--- a/notebooks/.ipynb_checkpoints/stacking-regression-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/stacking-regression-checkpoint.ipynb
@@ -8,7 +8,8 @@
    "source": [
     "import os\n",
     "\n",
-    "os.chdir('/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study/src')"
+    "os.chdir('/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study/src')\n",
+    "#os.chdir('/Users/hamiddimyati/Documents/codes/stacking-regression-comparative-study/src')"
    ]
   },
   {
@@ -24,7 +25,7 @@
     "import matplotlib.gridspec as gridspec\n",
     "plt.style.use('ggplot')\n",
     "import seaborn as sns\n",
-    "import scipy.stats as sc\n",
+    "#import scipy.stats as sc\n",
     "import time\n",
     "\n",
     "from sklearn.preprocessing import OneHotEncoder, StandardScaler, RobustScaler\n",
@@ -42,16 +43,103 @@
     "from lightgbm import LGBMRegressor\n",
     "from catboost import CatBoostRegressor\n",
     "\n",
-    "from skoptimize import RandomSearch, LinearSearch, DifferentialEvolution, GeneticAlgorithm, ParticleSwarmOptimization, SimulatedAnnealing, ArtificialFishSwarm"
+    "from skoptimize import LinearSearch, DifferentialEvolution, GeneticAlgorithm, ParticleSwarmOptimization, SimulatedAnnealing, GreedySearch"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dir = \"/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study\""
+    "base_models = {'LinearRegression': LinearRegression(),\n",
+    "              'Ridge': Ridge(),\n",
+    "              'Lasso': Lasso(),\n",
+    "              'KernelRidge': KernelRidge(kernel='rbf'),\n",
+    "              'KNeighborsRegressor': KNeighborsRegressor(),\n",
+    "              'MLPRegressor': MLPRegressor(max_iter=1000),\n",
+    "              'SVR': SVR(),\n",
+    "              'DecisionTreeRegressor': DecisionTreeRegressor(),\n",
+    "              #'Earth': Earth(),\n",
+    "              'BaggingRegressor': BaggingRegressor(),\n",
+    "              'RandomForestRegressor': RandomForestRegressor(),\n",
+    "              'GradientBoostingRegressor': GradientBoostingRegressor(),\n",
+    "              'XGBRegressor': XGBRegressor(),\n",
+    "              'LGBMRegressor': LGBMRegressor(),\n",
+    "              'CatBoostRegressor': CatBoostRegressor(verbose=False)}\n",
+    "\n",
+    "meta_models = {'LinearRegression': LinearRegression(),\n",
+    "               'Ridge': Ridge(),\n",
+    "               'LinearSearch': LinearSearch(non_negative=True, sum_to_one=False), \n",
+    "               'DifferentialEvolution': DifferentialEvolution(size_pop=1000, max_iter=1000, non_negative=False, sum_to_one=False), \n",
+    "               'GeneticAlgorithm': GeneticAlgorithm(size_pop=1000, max_iter=1000, non_negative=False, sum_to_one=False), \n",
+    "               'ParticleSwarmOptimization': ParticleSwarmOptimization(pop=1000, max_iter=1000, w=0.8, c1=0.5, c2=0.5, non_negative=False, sum_to_one=False), \n",
+    "               'SimulatedAnnealing': SimulatedAnnealing(T_max=1, T_min=1e-9, L=500, max_stay_counter=1000), \n",
+    "               'GreedySearch': GreedySearch(convergence='manhattan', epsilon=1e-2)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def base_cv_learning(model, X_train, y_train, cv_split):\n",
+    "    cv = KFold(n_splits=cv_split)\n",
+    "    rmse = []\n",
+    "    mae = []\n",
+    "    r2 = []\n",
+    "    predictions = np.array([])\n",
+    "    dict_result = {}\n",
+    "    for subtrain_index, subtest_index in cv.split(X_train):\n",
+    "        X_subtrain, X_subtest = X_train.iloc[subtrain_index], X_train.iloc[subtest_index]\n",
+    "        y_subtrain, y_subtest = y_train.iloc[subtrain_index], y_train.iloc[subtest_index]\n",
+    "        \n",
+    "        model.fit(X_subtrain, y_subtrain.to_numpy().ravel())\n",
+    "        y_subpred = model.predict(X_subtest)\n",
+    "        predictions = np.append(predictions, y_subpred)\n",
+    "        \n",
+    "        \n",
+    "        rmse.append(mean_squared_error(y_subtest, y_subpred, squared=False))\n",
+    "        mae.append(mean_absolute_error(y_subtest, y_subpred))\n",
+    "        r2.append(r2_score(y_subtest, y_subpred))\n",
+    "    dict_result['RMSE'] = round(np.mean(rmse), 3)\n",
+    "    dict_result['MAE'] = round(np.mean(mae), 3)\n",
+    "    dict_result['R2'] = round(np.mean(r2), 3)\n",
+    "    \n",
+    "    return predictions, dict_result\n",
+    "\n",
+    "def meta_cv_learning(model, X_train, y_train, cv_split):\n",
+    "    cv = KFold(n_splits=cv_split)\n",
+    "    rmse = []\n",
+    "    mae = []\n",
+    "    r2 = []\n",
+    "    dict_result = {}\n",
+    "    for subtrain_index, subtest_index in cv.split(X_train):\n",
+    "        X_subtrain, X_subtest = X_train.iloc[subtrain_index].to_numpy(), X_train.iloc[subtest_index].to_numpy()\n",
+    "        y_subtrain, y_subtest = y_train.iloc[subtrain_index].to_numpy().ravel(), y_train.iloc[subtest_index].to_numpy().ravel()\n",
+    "        \n",
+    "        model.fit(X_subtrain, y_subtrain)\n",
+    "        y_subpred = model.predict(X_subtest)\n",
+    "        \n",
+    "        rmse.append(mean_squared_error(y_subtest, y_subpred, squared=False))\n",
+    "        mae.append(mean_absolute_error(y_subtest, y_subpred))\n",
+    "        r2.append(r2_score(y_subtest, y_subpred))\n",
+    "    dict_result['RMSE'] = round(np.mean(rmse), 3)\n",
+    "    dict_result['MAE'] = round(np.mean(mae), 3)\n",
+    "    dict_result['R2'] = round(np.mean(r2), 3)\n",
+    "    \n",
+    "    return dict_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study\"\n",
+    "#path = \"/Users/hamiddimyati/Documents/codes/stacking-regression-comparative-study\""
    ]
   },
   {
@@ -63,16 +151,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "seoul_bike = pd.read_csv(dir + \"/data/SeoulBikeData.csv\", header=0, encoding= 'unicode_escape')"
+    "seoul_bike = pd.read_csv(path + \"/data/SeoulBikeData.csv\", header=0, encoding= 'unicode_escape')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +224,7 @@
        "Text(0.5, 1.0, 'Snowfall (cm)')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -181,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -201,7 +289,7 @@
        "Text(0.5, 1.0, 'Hour')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -244,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -357,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -386,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,24 +497,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "meta_models = {'LinearRegression': LinearRegression(),\n",
     "               'Ridge': Ridge(),\n",
-    "               'RandomSearch': RandomSearch(n_iter=1000), \n",
-    "               'LinearSearch': LinearSearch(non_negative=True, sum_to_one=True), \n",
-    "               'DifferentialEvolution': DifferentialEvolution(size_pop=1000, max_iter=100, non_negative=False, sum_to_one=False), \n",
-    "               'GeneticAlgorithm': GeneticAlgorithm(size_pop=1000, max_iter=100, non_negative=False, sum_to_one=False), \n",
-    "               'ParticleSwarmOptimization': ParticleSwarmOptimization(pop=1000, max_iter=100, w=0.8, c1=0.5, c2=0.5, non_negative=False, sum_to_one=False), \n",
-    "               'SimulatedAnnealing': SimulatedAnnealing(T_max=1, T_min=1e-9, L=300, max_stay_counter=100), \n",
-    "               'ArtificialFishSwarm': ArtificialFishSwarm(size_pop=500, max_iter=100, max_try_num=100, step=0.5, visual=0.3, q=0.98, delta=0.5)}"
+    "               'LinearSearch': LinearSearch(non_negative=True, sum_to_one=False), \n",
+    "               'DifferentialEvolution': DifferentialEvolution(size_pop=1000, max_iter=1000, non_negative=False, sum_to_one=False), \n",
+    "               'GeneticAlgorithm': GeneticAlgorithm(size_pop=1000, max_iter=1000, non_negative=False, sum_to_one=False), \n",
+    "               'ParticleSwarmOptimization': ParticleSwarmOptimization(pop=1000, max_iter=1000, w=0.8, c1=0.5, c2=0.5, non_negative=False, sum_to_one=False), \n",
+    "               'SimulatedAnnealing': SimulatedAnnealing(T_max=1, T_min=1e-9, L=500, max_stay_counter=1000), \n",
+    "               'GreedySearch': GreedySearch(convergence='manhattan', epsilon=1e-2)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -434,10 +521,25 @@
     "base_train_prediction = {}\n",
     "base_train_prediction['target'] = y_train.to_numpy().ravel()\n",
     "base_test_prediction = {}\n",
-    "base_test_prediction['target'] = y_test.to_numpy().ravel()\n",
-    "\n",
+    "base_test_prediction['target'] = y_test.to_numpy().ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "base_cv_performance = {}\n",
-    "base_test_performance = {}\n",
+    "base_test_performance = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "meta_cv_performance = {}\n",
     "meta_test_performance = {}"
    ]
@@ -458,12 +560,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[15:18:36] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
-      "[15:18:36] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
-      "[15:18:36] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
-      "[15:18:37] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
-      "[15:18:37] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
-      "[15:18:37] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+      "[23:05:37] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:39] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
      ]
     }
    ],
@@ -503,13 +605,13 @@
        " 'Lasso': {'RMSE': 431.885, 'MAE': 322.145, 'R2': 0.55},\n",
        " 'KernelRidge': {'RMSE': 318.111, 'MAE': 216.826, 'R2': 0.756},\n",
        " 'KNeighborsRegressor': {'RMSE': 308.562, 'MAE': 196.401, 'R2': 0.77},\n",
-       " 'MLPRegressor': {'RMSE': 320.947, 'MAE': 221.453, 'R2': 0.751},\n",
+       " 'MLPRegressor': {'RMSE': 321.845, 'MAE': 222.441, 'R2': 0.75},\n",
        " 'SVR': {'RMSE': 541.74, 'MAE': 370.645, 'R2': 0.293},\n",
-       " 'DecisionTreeRegressor': {'RMSE': 310.32, 'MAE': 180.758, 'R2': 0.767},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 309.28, 'MAE': 181.199, 'R2': 0.769},\n",
        " 'Earth': {'RMSE': 429.335, 'MAE': 321.446, 'R2': 0.556},\n",
-       " 'BaggingRegressor': {'RMSE': 237.335, 'MAE': 144.388, 'R2': 0.864},\n",
-       " 'RandomForestRegressor': {'RMSE': 224.347, 'MAE': 136.893, 'R2': 0.878},\n",
-       " 'GradientBoostingRegressor': {'RMSE': 252.352, 'MAE': 168.397, 'R2': 0.846},\n",
+       " 'BaggingRegressor': {'RMSE': 232.851, 'MAE': 142.987, 'R2': 0.869},\n",
+       " 'RandomForestRegressor': {'RMSE': 224.625, 'MAE': 136.939, 'R2': 0.878},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 252.347, 'MAE': 168.352, 'R2': 0.846},\n",
        " 'XGBRegressor': {'RMSE': 251.638, 'MAE': 168.246, 'R2': 0.847},\n",
        " 'LGBMRegressor': {'RMSE': 213.927, 'MAE': 133.812, 'R2': 0.889},\n",
        " 'CatBoostRegressor': {'RMSE': 211.156, 'MAE': 132.758, 'R2': 0.892}}"
@@ -537,13 +639,13 @@
        " 'Lasso': {'RMSE': 438.698, 'MAE': 327.013, 'R2': 0.54},\n",
        " 'KernelRidge': {'RMSE': 333.564, 'MAE': 227.02, 'R2': 0.734},\n",
        " 'KNeighborsRegressor': {'RMSE': 306.489, 'MAE': 192.451, 'R2': 0.776},\n",
-       " 'MLPRegressor': {'RMSE': 331.478, 'MAE': 225.769, 'R2': 0.738},\n",
+       " 'MLPRegressor': {'RMSE': 328.488, 'MAE': 222.467, 'R2': 0.742},\n",
        " 'SVR': {'RMSE': 535.479, 'MAE': 365.982, 'R2': 0.315},\n",
-       " 'DecisionTreeRegressor': {'RMSE': 319.5, 'MAE': 187.512, 'R2': 0.756},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 320.243, 'MAE': 186.488, 'R2': 0.755},\n",
        " 'Earth': {'RMSE': 436.442, 'MAE': 326.4, 'R2': 0.545},\n",
-       " 'BaggingRegressor': {'RMSE': 242.719, 'MAE': 146.83, 'R2': 0.859},\n",
-       " 'RandomForestRegressor': {'RMSE': 234.025, 'MAE': 141.596, 'R2': 0.869},\n",
-       " 'GradientBoostingRegressor': {'RMSE': 269.779, 'MAE': 181.939, 'R2': 0.826},\n",
+       " 'BaggingRegressor': {'RMSE': 248.349, 'MAE': 149.09, 'R2': 0.853},\n",
+       " 'RandomForestRegressor': {'RMSE': 232.604, 'MAE': 140.553, 'R2': 0.871},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 269.575, 'MAE': 181.896, 'R2': 0.826},\n",
        " 'XGBRegressor': {'RMSE': 270.605, 'MAE': 180.463, 'R2': 0.825},\n",
        " 'LGBMRegressor': {'RMSE': 218.842, 'MAE': 136.099, 'R2': 0.886},\n",
        " 'CatBoostRegressor': {'RMSE': 213.575, 'MAE': 132.441, 'R2': 0.891}}"
@@ -610,6 +712,13 @@
     "    meta_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "100 iteration"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 26,
@@ -618,15 +727,14 @@
     {
      "data": {
       "text/plain": [
-       "{'LinearRegression': {'RMSE': 207.079, 'MAE': 126.954, 'R2': 0.896},\n",
-       " 'Ridge': {'RMSE': 207.078, 'MAE': 126.955, 'R2': 0.896},\n",
-       " 'RandomSearch': {'RMSE': 233.439, 'MAE': 152.752, 'R2': 0.868},\n",
-       " 'LinearSearch': {'RMSE': 210.326, 'MAE': 130.852, 'R2': 0.893},\n",
-       " 'DifferentialEvolution': {'RMSE': 221.86, 'MAE': 142.577, 'R2': 0.881},\n",
-       " 'GeneticAlgorithm': {'RMSE': 209.236, 'MAE': 128.796, 'R2': 0.894},\n",
-       " 'ParticleSwarmOptimization': {'RMSE': 207.411, 'MAE': 127.659, 'R2': 0.896},\n",
-       " 'SimulatedAnnealing': {'RMSE': 233.989, 'MAE': 150.206, 'R2': 0.868},\n",
-       " 'ArtificialFishSwarm': {'RMSE': 207.956, 'MAE': 128.274, 'R2': 0.895}}"
+       "{'LinearRegression': {'RMSE': 207.055, 'MAE': 127.009, 'R2': 0.896},\n",
+       " 'Ridge': {'RMSE': 207.055, 'MAE': 127.009, 'R2': 0.896},\n",
+       " 'LinearSearch': {'RMSE': 209.604, 'MAE': 130.455, 'R2': 0.894},\n",
+       " 'DifferentialEvolution': {'RMSE': 208.267, 'MAE': 129.598, 'R2': 0.895},\n",
+       " 'GeneticAlgorithm': {'RMSE': 206.959, 'MAE': 127.092, 'R2': 0.897},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 207.033, 'MAE': 127.102, 'R2': 0.896},\n",
+       " 'SimulatedAnnealing': {'RMSE': 223.145, 'MAE': 143.772, 'R2': 0.879},\n",
+       " 'GreedySearch': {'RMSE': 209.942, 'MAE': 129.521, 'R2': 0.893}}"
       ]
      },
      "execution_count": 26,
@@ -646,15 +754,14 @@
     {
      "data": {
       "text/plain": [
-       "{'LinearRegression': {'RMSE': 206.07, 'MAE': 124.334, 'R2': 0.899},\n",
-       " 'Ridge': {'RMSE': 206.069, 'MAE': 124.334, 'R2': 0.899},\n",
-       " 'RandomSearch': {'RMSE': 241.448, 'MAE': 153.255, 'R2': 0.861},\n",
-       " 'LinearSearch': {'RMSE': 219.481, 'MAE': 136.046, 'R2': 0.885},\n",
-       " 'DifferentialEvolution': {'RMSE': 218.886, 'MAE': 142.386, 'R2': 0.886},\n",
-       " 'GeneticAlgorithm': {'RMSE': 207.441, 'MAE': 127.476, 'R2': 0.897},\n",
-       " 'ParticleSwarmOptimization': {'RMSE': 206.927, 'MAE': 125.606, 'R2': 0.898},\n",
-       " 'SimulatedAnnealing': {'RMSE': 224.179, 'MAE': 148.721, 'R2': 0.88},\n",
-       " 'ArtificialFishSwarm': {'RMSE': 208.251, 'MAE': 127.16, 'R2': 0.896}}"
+       "{'LinearRegression': {'RMSE': 206.19, 'MAE': 124.295, 'R2': 0.898},\n",
+       " 'Ridge': {'RMSE': 206.189, 'MAE': 124.295, 'R2': 0.898},\n",
+       " 'LinearSearch': {'RMSE': 221.178, 'MAE': 136.426, 'R2': 0.883},\n",
+       " 'DifferentialEvolution': {'RMSE': 211.44, 'MAE': 128.732, 'R2': 0.893},\n",
+       " 'GeneticAlgorithm': {'RMSE': 206.976, 'MAE': 125.39, 'R2': 0.898},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 206.269, 'MAE': 124.592, 'R2': 0.898},\n",
+       " 'SimulatedAnnealing': {'RMSE': 211.439, 'MAE': 133.123, 'R2': 0.893},\n",
+       " 'GreedySearch': {'RMSE': 215.868, 'MAE': 131.394, 'R2': 0.889}}"
       ]
      },
      "execution_count": 27,
@@ -666,6 +773,31 @@
     "meta_test_performance"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1000 iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_test_performance"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -675,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -711,39 +843,8 @@
     "    print(num_weights)\n",
     "    print(weights)\n",
     "\n",
-    "    return weights/float(num_weights)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "took 2.8214519023895264 seconds.\n",
-      "100\n",
-      "[ 0  0  0  0  9  0  0  2  0  0 30  0  0 21 38]\n",
-      "took 5.417552709579468 seconds.\n",
-      "187\n",
-      "[ 0  0  0  0 16  0  0  4  0  0 55  0  0 40 72]\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'str'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-42-2bcb9aebfcd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mr2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mmeta_test_performance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GreedyStacking'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'RMSE'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mmeta_test_performance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GreedyStacking'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'MAE'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmae\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mmeta_test_performance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GreedyStacking'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'R2'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'str'"
-     ]
-    }
-   ],
-   "source": [
+    "    return weights/float(num_weights)\n",
+    "\n",
     "weight1 = greedy_opt(X_train_meta.to_numpy(), y_train_meta.to_numpy(), metric=metric_spearman, converged=conv_manhattan, eps=1e-2)\n",
     "weight2 = greedy_opt(X_train_meta.to_numpy(), y_train_meta.to_numpy(), metric=metric_spearman, converged=conv_euclid, eps=1e-2)\n",
     "\n",
@@ -751,101 +852,966 @@
     "\n",
     "rmse = mean_squared_error(y_test_meta, y_pred, squared=False)\n",
     "mae = mean_absolute_error(y_test_meta, y_pred)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "meta_test_performance['GreedyStacking'.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
-    "meta_test_performance['GreedyStacking'.__class__.__name__]['MAE'] = round(mae, 3)\n",
-    "meta_test_performance['GreedyStacking'.__class__.__name__]['R2'] = round(r2, 3)"
+    "r2 = r2_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ===================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection (SKIPPED!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameter Tuning (SKIPPED!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pre-process Slump Test Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
-    "meta_test_performance"
+    "slump = pd.read_csv(path + \"/data/slump_test.data\", header=0, encoding= 'unicode_escape')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 108,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "215.74350006437393"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "slump.rename(columns={'Fly ash': 'FlyAsh', 'Coarse Aggr.': 'CoarseAggr', 'Fine Aggr.': 'FineAggr', 'SLUMP(cm)': 'Slump' , 'FLOW(cm)' : 'Flow' , 'Compressive Strength (28-day)(Mpa)': 'CompressiveStr'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slump.drop(columns=['No'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "rmse"
+    "#slump.drop(slump.tail(1).index,inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "131.55150688629558"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 103 entries, 0 to 102\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column          Non-Null Count  Dtype  \n",
+      "---  ------          --------------  -----  \n",
+      " 0   Cement          103 non-null    float64\n",
+      " 1   Slag            103 non-null    float64\n",
+      " 2   FlyAsh          103 non-null    float64\n",
+      " 3   Water           103 non-null    float64\n",
+      " 4   SP              103 non-null    float64\n",
+      " 5   CoarseAggr      103 non-null    float64\n",
+      " 6   FineAggr        103 non-null    float64\n",
+      " 7   Slump           103 non-null    float64\n",
+      " 8   Flow            103 non-null    float64\n",
+      " 9   CompressiveStr  103 non-null    float64\n",
+      "dtypes: float64(10)\n",
+      "memory usage: 8.2 KB\n"
+     ]
     }
    ],
    "source": [
-    "mae"
+    "slump.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Cement</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>229.894175</td>\n",
+       "      <td>78.877230</td>\n",
+       "      <td>137.00</td>\n",
+       "      <td>152.00</td>\n",
+       "      <td>248.00</td>\n",
+       "      <td>303.900</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slag</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>77.973786</td>\n",
+       "      <td>60.461363</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>125.000</td>\n",
+       "      <td>193.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FlyAsh</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>149.014563</td>\n",
+       "      <td>85.418080</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>115.50</td>\n",
+       "      <td>164.00</td>\n",
+       "      <td>235.950</td>\n",
+       "      <td>260.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Water</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>197.167961</td>\n",
+       "      <td>20.208158</td>\n",
+       "      <td>160.00</td>\n",
+       "      <td>180.00</td>\n",
+       "      <td>196.00</td>\n",
+       "      <td>209.500</td>\n",
+       "      <td>240.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SP</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>8.539806</td>\n",
+       "      <td>2.807530</td>\n",
+       "      <td>4.40</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>10.000</td>\n",
+       "      <td>19.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CoarseAggr</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>883.978641</td>\n",
+       "      <td>88.391393</td>\n",
+       "      <td>708.00</td>\n",
+       "      <td>819.50</td>\n",
+       "      <td>879.00</td>\n",
+       "      <td>952.800</td>\n",
+       "      <td>1049.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FineAggr</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>739.604854</td>\n",
+       "      <td>63.342117</td>\n",
+       "      <td>640.60</td>\n",
+       "      <td>684.50</td>\n",
+       "      <td>742.70</td>\n",
+       "      <td>788.000</td>\n",
+       "      <td>902.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slump</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>18.048544</td>\n",
+       "      <td>8.750844</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>14.50</td>\n",
+       "      <td>21.50</td>\n",
+       "      <td>24.000</td>\n",
+       "      <td>29.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Flow</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>49.610680</td>\n",
+       "      <td>17.568610</td>\n",
+       "      <td>20.00</td>\n",
+       "      <td>38.50</td>\n",
+       "      <td>54.00</td>\n",
+       "      <td>63.750</td>\n",
+       "      <td>78.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CompressiveStr</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>36.039417</td>\n",
+       "      <td>7.838232</td>\n",
+       "      <td>17.19</td>\n",
+       "      <td>30.90</td>\n",
+       "      <td>35.52</td>\n",
+       "      <td>41.205</td>\n",
+       "      <td>58.53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "0.8888279162237089"
+       "                count        mean        std     min     25%     50%      75%  \\\n",
+       "Cement          103.0  229.894175  78.877230  137.00  152.00  248.00  303.900   \n",
+       "Slag            103.0   77.973786  60.461363    0.00    0.05  100.00  125.000   \n",
+       "FlyAsh          103.0  149.014563  85.418080    0.00  115.50  164.00  235.950   \n",
+       "Water           103.0  197.167961  20.208158  160.00  180.00  196.00  209.500   \n",
+       "SP              103.0    8.539806   2.807530    4.40    6.00    8.00   10.000   \n",
+       "CoarseAggr      103.0  883.978641  88.391393  708.00  819.50  879.00  952.800   \n",
+       "FineAggr        103.0  739.604854  63.342117  640.60  684.50  742.70  788.000   \n",
+       "Slump           103.0   18.048544   8.750844    0.00   14.50   21.50   24.000   \n",
+       "Flow            103.0   49.610680  17.568610   20.00   38.50   54.00   63.750   \n",
+       "CompressiveStr  103.0   36.039417   7.838232   17.19   30.90   35.52   41.205   \n",
+       "\n",
+       "                    max  \n",
+       "Cement           374.00  \n",
+       "Slag             193.00  \n",
+       "FlyAsh           260.00  \n",
+       "Water            240.00  \n",
+       "SP                19.00  \n",
+       "CoarseAggr      1049.90  \n",
+       "FineAggr         902.00  \n",
+       "Slump             29.00  \n",
+       "Flow              78.00  \n",
+       "CompressiveStr    58.53  "
       ]
      },
-     "execution_count": 45,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "r2"
+    "slump.describe().transpose()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 122,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# ===================================================================="
+    "X = slump[['Cement','Slag','FlyAsh','Water','SP','CoarseAggr','FineAggr']]\n",
+    "y = slump[['Slump']]\n",
+    "#y = slump[['Slump','Flow','CompressiveStr']]"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 139,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Feature Selection (SKIPPED!)"
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=333)\n",
+    "base_train_prediction = {}\n",
+    "base_train_prediction['target'] = y_train.to_numpy().ravel()\n",
+    "base_test_prediction = {}\n",
+    "base_test_prediction['target'] = y_test.to_numpy().ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_cv_performance = {}\n",
+    "base_test_performance = {}\n",
+    "meta_cv_performance = {}\n",
+    "meta_test_performance = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in base_models:\n",
+    "    model = base_models[m]\n",
+    "    base_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    prediction, performance = base_cv_learning(model, X_train, y_train, 5)\n",
+    "    base_train_prediction[model.__class__.__name__] = prediction\n",
+    "    base_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    try:\n",
+    "        base_test_prediction[model.__class__.__name__] = [items[0] for items in y_pred.tolist()]\n",
+    "    except:\n",
+    "        base_test_prediction[model.__class__.__name__] = y_pred.tolist()\n",
+    "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    base_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 7.934, 'MAE': 6.498, 'R2': -0.571},\n",
+       " 'Ridge': {'RMSE': 7.934, 'MAE': 6.497, 'R2': -0.57},\n",
+       " 'Lasso': {'RMSE': 7.804, 'MAE': 6.401, 'R2': -0.523},\n",
+       " 'KernelRidge': {'RMSE': 20.051, 'MAE': 18.283, 'R2': -16.7},\n",
+       " 'KNeighborsRegressor': {'RMSE': 8.001, 'MAE': 6.033, 'R2': -0.255},\n",
+       " 'MLPRegressor': {'RMSE': 35.985, 'MAE': 32.608, 'R2': -41.441},\n",
+       " 'SVR': {'RMSE': 8.723, 'MAE': 6.494, 'R2': -0.381},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 9.61, 'MAE': 6.629, 'R2': -1.828},\n",
+       " 'BaggingRegressor': {'RMSE': 7.598, 'MAE': 5.294, 'R2': -0.53},\n",
+       " 'RandomForestRegressor': {'RMSE': 7.217, 'MAE': 5.285, 'R2': -0.262},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 8.402, 'MAE': 6.041, 'R2': -0.951},\n",
+       " 'XGBRegressor': {'RMSE': 7.971, 'MAE': 5.678, 'R2': -0.463},\n",
+       " 'LGBMRegressor': {'RMSE': 7.534, 'MAE': 5.947, 'R2': -0.194},\n",
+       " 'CatBoostRegressor': {'RMSE': 7.55, 'MAE': 5.446, 'R2': -0.341}}"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 9.095, 'MAE': 7.076, 'R2': -0.021},\n",
+       " 'Ridge': {'RMSE': 9.094, 'MAE': 7.076, 'R2': -0.021},\n",
+       " 'Lasso': {'RMSE': 8.987, 'MAE': 7.067, 'R2': 0.003},\n",
+       " 'KernelRidge': {'RMSE': 19.151, 'MAE': 16.904, 'R2': -3.526},\n",
+       " 'KNeighborsRegressor': {'RMSE': 11.173, 'MAE': 8.326, 'R2': -0.54},\n",
+       " 'MLPRegressor': {'RMSE': 59.419, 'MAE': 57.623, 'R2': -42.567},\n",
+       " 'SVR': {'RMSE': 10.264, 'MAE': 6.858, 'R2': -0.3},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 11.013, 'MAE': 7.81, 'R2': -0.497},\n",
+       " 'BaggingRegressor': {'RMSE': 9.079, 'MAE': 6.569, 'R2': -0.017},\n",
+       " 'RandomForestRegressor': {'RMSE': 8.396, 'MAE': 6.231, 'R2': 0.13},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 9.788, 'MAE': 6.808, 'R2': -0.182},\n",
+       " 'XGBRegressor': {'RMSE': 9.837, 'MAE': 6.596, 'R2': -0.194},\n",
+       " 'LGBMRegressor': {'RMSE': 8.981, 'MAE': 6.878, 'R2': 0.005},\n",
+       " 'CatBoostRegressor': {'RMSE': 9.116, 'MAE': 6.394, 'R2': -0.025}}"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_test_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train_meta = pd.DataFrame(base_train_prediction)\n",
+    "y_train_meta = df_train_meta[['target']]\n",
+    "X_train_meta = df_train_meta.drop(columns=['target'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_meta = pd.DataFrame(base_test_prediction)\n",
+    "y_test_meta = df_test_meta[['target']].to_numpy().ravel()\n",
+    "X_test_meta = df_test_meta.drop(columns=['target'], axis=1).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for m in meta_models:\n",
+    "    model = meta_models[m]\n",
+    "    meta_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    performance = meta_cv_learning(model, X_train_meta, y_train_meta, 5)\n",
+    "    meta_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train_meta.to_numpy(), y_train_meta.to_numpy().ravel())\n",
+    "    y_pred = model.predict(X_test_meta)\n",
+    "    rmse = mean_squared_error(y_test_meta, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test_meta, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    meta_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 9.061, 'MAE': 6.883, 'R2': -1.137},\n",
+       " 'Ridge': {'RMSE': 8.699, 'MAE': 6.673, 'R2': -1.001},\n",
+       " 'LinearSearch': {'RMSE': 7.791, 'MAE': 6.182, 'R2': -0.572},\n",
+       " 'DifferentialEvolution': {'RMSE': 8.526, 'MAE': 6.448, 'R2': -1.069},\n",
+       " 'GeneticAlgorithm': {'RMSE': 8.592, 'MAE': 6.457, 'R2': -1.078},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 8.613, 'MAE': 6.566, 'R2': -1.126},\n",
+       " 'SimulatedAnnealing': {'RMSE': 8.233, 'MAE': 6.421, 'R2': -0.728},\n",
+       " 'GreedySearch': {'RMSE': 15.106, 'MAE': 14.0, 'R2': -10.286}}"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 8.215, 'MAE': 7.061, 'R2': 0.167},\n",
+       " 'Ridge': {'RMSE': 8.018, 'MAE': 6.87, 'R2': 0.207},\n",
+       " 'LinearSearch': {'RMSE': 8.31, 'MAE': 6.35, 'R2': 0.148},\n",
+       " 'DifferentialEvolution': {'RMSE': 8.348, 'MAE': 6.838, 'R2': 0.14},\n",
+       " 'GeneticAlgorithm': {'RMSE': 8.294, 'MAE': 6.931, 'R2': 0.151},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 8.386, 'MAE': 6.979, 'R2': 0.132},\n",
+       " 'SimulatedAnnealing': {'RMSE': 8.323, 'MAE': 6.634, 'R2': 0.145},\n",
+       " 'GreedySearch': {'RMSE': 14.201, 'MAE': 12.813, 'R2': -1.489}}"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_test_performance"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Parameter Tuning (SKIPPED!)"
+    "# Pre-process Slump Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock = pd.read_csv(path + \"/data/Stock Portofolio.csv\", header=0, encoding= 'unicode_escape')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock.rename(columns=dict(zip(stock.columns, ['largeBP', 'largeROE', 'largeSP', 'largeReturn', 'largeValue', 'smallRisk', 'Return'])), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>largeBP</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeROE</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeSP</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeReturn</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeValue</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>smallRisk</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Return</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>14.923810</td>\n",
+       "      <td>2.787224</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>15.300</td>\n",
+       "      <td>17.0000</td>\n",
+       "      <td>19.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count       mean       std  min   25%     50%      75%   max\n",
+       "largeBP       63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeROE      63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeSP       63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeReturn   63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeValue    63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "smallRisk     63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "Return        63.0  14.923810  2.787224  7.0  13.8  15.300  17.0000  19.5"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 63 entries, 0 to 62\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   largeBP      63 non-null     float64\n",
+      " 1   largeROE     63 non-null     float64\n",
+      " 2   largeSP      63 non-null     float64\n",
+      " 3   largeReturn  63 non-null     float64\n",
+      " 4   largeValue   63 non-null     float64\n",
+      " 5   smallRisk    63 non-null     float64\n",
+      " 6   Return       63 non-null     float64\n",
+      "dtypes: float64(7)\n",
+      "memory usage: 3.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "stock.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = stock[['largeBP','largeROE','largeSP','largeReturn','largeValue','smallRisk']]\n",
+    "y = stock[['Return']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=333)\n",
+    "base_train_prediction = {}\n",
+    "base_train_prediction['target'] = y_train.to_numpy().ravel()\n",
+    "base_test_prediction = {}\n",
+    "base_test_prediction['target'] = y_test.to_numpy().ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_cv_performance = {}\n",
+    "base_test_performance = {}\n",
+    "meta_cv_performance = {}\n",
+    "meta_test_performance = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in base_models:\n",
+    "    model = base_models[m]\n",
+    "    base_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    prediction, performance = base_cv_learning(model, X_train, y_train, 5)\n",
+    "    base_train_prediction[model.__class__.__name__] = prediction\n",
+    "    base_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    try:\n",
+    "        base_test_prediction[model.__class__.__name__] = [items[0] for items in y_pred.tolist()]\n",
+    "    except:\n",
+    "        base_test_prediction[model.__class__.__name__] = y_pred.tolist()\n",
+    "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    base_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 2.282, 'MAE': 1.874, 'R2': 0.272},\n",
+       " 'Ridge': {'RMSE': 2.12, 'MAE': 1.782, 'R2': 0.381},\n",
+       " 'Lasso': {'RMSE': 2.728, 'MAE': 2.147, 'R2': -0.025},\n",
+       " 'KernelRidge': {'RMSE': 2.056, 'MAE': 1.694, 'R2': 0.424},\n",
+       " 'KNeighborsRegressor': {'RMSE': 1.959, 'MAE': 1.432, 'R2': 0.463},\n",
+       " 'MLPRegressor': {'RMSE': 1.895, 'MAE': 1.629, 'R2': 0.489},\n",
+       " 'SVR': {'RMSE': 1.888, 'MAE': 1.342, 'R2': 0.51},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 1.73, 'MAE': 1.498, 'R2': 0.585},\n",
+       " 'BaggingRegressor': {'RMSE': 1.861, 'MAE': 1.453, 'R2': 0.525},\n",
+       " 'RandomForestRegressor': {'RMSE': 1.655, 'MAE': 1.332, 'R2': 0.62},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 1.163, 'MAE': 0.963, 'R2': 0.805},\n",
+       " 'XGBRegressor': {'RMSE': 1.559, 'MAE': 1.174, 'R2': 0.664},\n",
+       " 'LGBMRegressor': {'RMSE': 2.699, 'MAE': 2.114, 'R2': -0.04},\n",
+       " 'CatBoostRegressor': {'RMSE': 1.545, 'MAE': 1.172, 'R2': 0.672}}"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 1.49, 'MAE': 1.267, 'R2': 0.719},\n",
+       " 'Ridge': {'RMSE': 1.842, 'MAE': 1.611, 'R2': 0.571},\n",
+       " 'Lasso': {'RMSE': 2.879, 'MAE': 2.307, 'R2': -0.049},\n",
+       " 'KernelRidge': {'RMSE': 2.119, 'MAE': 1.723, 'R2': 0.432},\n",
+       " 'KNeighborsRegressor': {'RMSE': 1.751, 'MAE': 1.489, 'R2': 0.612},\n",
+       " 'MLPRegressor': {'RMSE': 1.572, 'MAE': 1.385, 'R2': 0.688},\n",
+       " 'SVR': {'RMSE': 1.822, 'MAE': 1.382, 'R2': 0.58},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 2.066, 'MAE': 1.777, 'R2': 0.46},\n",
+       " 'BaggingRegressor': {'RMSE': 1.705, 'MAE': 1.414, 'R2': 0.632},\n",
+       " 'RandomForestRegressor': {'RMSE': 1.724, 'MAE': 1.489, 'R2': 0.624},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 1.106, 'MAE': 0.897, 'R2': 0.845},\n",
+       " 'XGBRegressor': {'RMSE': 1.271, 'MAE': 1.006, 'R2': 0.796},\n",
+       " 'LGBMRegressor': {'RMSE': 1.505, 'MAE': 1.121, 'R2': 0.714},\n",
+       " 'CatBoostRegressor': {'RMSE': 1.54, 'MAE': 1.25, 'R2': 0.7}}"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_test_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train_meta = pd.DataFrame(base_train_prediction)\n",
+    "y_train_meta = df_train_meta[['target']]\n",
+    "X_train_meta = df_train_meta.drop(columns=['target'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_meta = pd.DataFrame(base_test_prediction)\n",
+    "y_test_meta = df_test_meta[['target']].to_numpy().ravel()\n",
+    "X_test_meta = df_test_meta.drop(columns=['target'], axis=1).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for m in meta_models:\n",
+    "    model = meta_models[m]\n",
+    "    meta_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    performance = meta_cv_learning(model, X_train_meta, y_train_meta, 5)\n",
+    "    meta_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train_meta.to_numpy(), y_train_meta.to_numpy().ravel())\n",
+    "    y_pred = model.predict(X_test_meta)\n",
+    "    rmse = mean_squared_error(y_test_meta, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test_meta, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    meta_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 1.558, 'MAE': 1.29, 'R2': 0.66},\n",
+       " 'Ridge': {'RMSE': 1.315, 'MAE': 1.096, 'R2': 0.758},\n",
+       " 'LinearSearch': {'RMSE': 1.365, 'MAE': 1.123, 'R2': 0.732},\n",
+       " 'DifferentialEvolution': {'RMSE': 1.473, 'MAE': 1.196, 'R2': 0.686},\n",
+       " 'GeneticAlgorithm': {'RMSE': 1.164, 'MAE': 0.981, 'R2': 0.814},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 1.326, 'MAE': 1.078, 'R2': 0.754},\n",
+       " 'SimulatedAnnealing': {'RMSE': 1.175, 'MAE': 0.981, 'R2': 0.804},\n",
+       " 'GreedySearch': {'RMSE': 2.076, 'MAE': 1.606, 'R2': 0.413}}"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 1.214, 'MAE': 1.025, 'R2': 0.814},\n",
+       " 'Ridge': {'RMSE': 1.123, 'MAE': 0.896, 'R2': 0.84},\n",
+       " 'LinearSearch': {'RMSE': 1.231, 'MAE': 0.992, 'R2': 0.809},\n",
+       " 'DifferentialEvolution': {'RMSE': 1.291, 'MAE': 1.02, 'R2': 0.789},\n",
+       " 'GeneticAlgorithm': {'RMSE': 1.057, 'MAE': 0.857, 'R2': 0.859},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 1.138, 'MAE': 0.906, 'R2': 0.836},\n",
+       " 'SimulatedAnnealing': {'RMSE': 1.477, 'MAE': 1.264, 'R2': 0.724},\n",
+       " 'GreedySearch': {'RMSE': 2.14, 'MAE': 1.797, 'R2': 0.421}}"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_test_performance"
    ]
   },
   {
diff --git a/notebooks/stacking-regression.ipynb b/notebooks/stacking-regression.ipynb
index 71da574..a366cd5 100644
--- a/notebooks/stacking-regression.ipynb
+++ b/notebooks/stacking-regression.ipynb
@@ -8,8 +8,8 @@
    "source": [
     "import os\n",
     "\n",
-    "#os.chdir('/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study/src')\n",
-    "os.chdir('/Users/hamiddimyati/Documents/codes/stacking-regression-comparative-study/src')"
+    "os.chdir('/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study/src')\n",
+    "#os.chdir('/Users/hamiddimyati/Documents/codes/stacking-regression-comparative-study/src')"
    ]
   },
   {
@@ -48,12 +48,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#path = \"/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study\"\n",
-    "path = \"/Users/hamiddimyati/Documents/codes/stacking-regression-comparative-study\""
+    "base_models = {'LinearRegression': LinearRegression(),\n",
+    "              'Ridge': Ridge(),\n",
+    "              'Lasso': Lasso(),\n",
+    "              'KernelRidge': KernelRidge(kernel='rbf'),\n",
+    "              'KNeighborsRegressor': KNeighborsRegressor(),\n",
+    "              'MLPRegressor': MLPRegressor(max_iter=1000),\n",
+    "              'SVR': SVR(),\n",
+    "              'DecisionTreeRegressor': DecisionTreeRegressor(),\n",
+    "              #'Earth': Earth(),\n",
+    "              'BaggingRegressor': BaggingRegressor(),\n",
+    "              'RandomForestRegressor': RandomForestRegressor(),\n",
+    "              'GradientBoostingRegressor': GradientBoostingRegressor(),\n",
+    "              'XGBRegressor': XGBRegressor(),\n",
+    "              'LGBMRegressor': LGBMRegressor(),\n",
+    "              'CatBoostRegressor': CatBoostRegressor(verbose=False)}\n",
+    "\n",
+    "meta_models = {'LinearRegression': LinearRegression(),\n",
+    "               'Ridge': Ridge(),\n",
+    "               'LinearSearch': LinearSearch(non_negative=True, sum_to_one=False), \n",
+    "               'DifferentialEvolution': DifferentialEvolution(size_pop=1000, max_iter=1000, non_negative=False, sum_to_one=False), \n",
+    "               'GeneticAlgorithm': GeneticAlgorithm(size_pop=1000, max_iter=1000, non_negative=False, sum_to_one=False), \n",
+    "               'ParticleSwarmOptimization': ParticleSwarmOptimization(pop=1000, max_iter=1000, w=0.8, c1=0.5, c2=0.5, non_negative=False, sum_to_one=False), \n",
+    "               'SimulatedAnnealing': SimulatedAnnealing(T_max=1, T_min=1e-9, L=500, max_stay_counter=1000), \n",
+    "               'GreedySearch': GreedySearch(convergence='manhattan', epsilon=1e-2)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def base_cv_learning(model, X_train, y_train, cv_split):\n",
+    "    cv = KFold(n_splits=cv_split)\n",
+    "    rmse = []\n",
+    "    mae = []\n",
+    "    r2 = []\n",
+    "    predictions = np.array([])\n",
+    "    dict_result = {}\n",
+    "    for subtrain_index, subtest_index in cv.split(X_train):\n",
+    "        X_subtrain, X_subtest = X_train.iloc[subtrain_index], X_train.iloc[subtest_index]\n",
+    "        y_subtrain, y_subtest = y_train.iloc[subtrain_index], y_train.iloc[subtest_index]\n",
+    "        \n",
+    "        model.fit(X_subtrain, y_subtrain.to_numpy().ravel())\n",
+    "        y_subpred = model.predict(X_subtest)\n",
+    "        predictions = np.append(predictions, y_subpred)\n",
+    "        \n",
+    "        \n",
+    "        rmse.append(mean_squared_error(y_subtest, y_subpred, squared=False))\n",
+    "        mae.append(mean_absolute_error(y_subtest, y_subpred))\n",
+    "        r2.append(r2_score(y_subtest, y_subpred))\n",
+    "    dict_result['RMSE'] = round(np.mean(rmse), 3)\n",
+    "    dict_result['MAE'] = round(np.mean(mae), 3)\n",
+    "    dict_result['R2'] = round(np.mean(r2), 3)\n",
+    "    \n",
+    "    return predictions, dict_result\n",
+    "\n",
+    "def meta_cv_learning(model, X_train, y_train, cv_split):\n",
+    "    cv = KFold(n_splits=cv_split)\n",
+    "    rmse = []\n",
+    "    mae = []\n",
+    "    r2 = []\n",
+    "    dict_result = {}\n",
+    "    for subtrain_index, subtest_index in cv.split(X_train):\n",
+    "        X_subtrain, X_subtest = X_train.iloc[subtrain_index].to_numpy(), X_train.iloc[subtest_index].to_numpy()\n",
+    "        y_subtrain, y_subtest = y_train.iloc[subtrain_index].to_numpy().ravel(), y_train.iloc[subtest_index].to_numpy().ravel()\n",
+    "        \n",
+    "        model.fit(X_subtrain, y_subtrain)\n",
+    "        y_subpred = model.predict(X_subtest)\n",
+    "        \n",
+    "        rmse.append(mean_squared_error(y_subtest, y_subpred, squared=False))\n",
+    "        mae.append(mean_absolute_error(y_subtest, y_subpred))\n",
+    "        r2.append(r2_score(y_subtest, y_subpred))\n",
+    "    dict_result['RMSE'] = round(np.mean(rmse), 3)\n",
+    "    dict_result['MAE'] = round(np.mean(mae), 3)\n",
+    "    dict_result['R2'] = round(np.mean(r2), 3)\n",
+    "    \n",
+    "    return dict_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"/Users/user/Documents/GitHub/II2202_Stacking-Regression-Comparative-Study\"\n",
+    "#path = \"/Users/hamiddimyati/Documents/codes/stacking-regression-comparative-study\""
    ]
   },
   {
@@ -65,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +224,7 @@
        "Text(0.5, 1.0, 'Snowfall (cm)')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -183,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -203,7 +289,7 @@
        "Text(0.5, 1.0, 'Hour')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -246,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -388,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,9 +553,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[23:05:37] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[23:05:39] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    }
+   ],
    "source": [
     "for m in base_models:\n",
     "    model = base_models[m]\n",
@@ -495,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -506,19 +605,19 @@
        " 'Lasso': {'RMSE': 431.885, 'MAE': 322.145, 'R2': 0.55},\n",
        " 'KernelRidge': {'RMSE': 318.111, 'MAE': 216.826, 'R2': 0.756},\n",
        " 'KNeighborsRegressor': {'RMSE': 308.562, 'MAE': 196.401, 'R2': 0.77},\n",
-       " 'MLPRegressor': {'RMSE': 321.869, 'MAE': 221.817, 'R2': 0.75},\n",
+       " 'MLPRegressor': {'RMSE': 321.845, 'MAE': 222.441, 'R2': 0.75},\n",
        " 'SVR': {'RMSE': 541.74, 'MAE': 370.645, 'R2': 0.293},\n",
-       " 'DecisionTreeRegressor': {'RMSE': 310.078, 'MAE': 181.548, 'R2': 0.768},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 309.28, 'MAE': 181.199, 'R2': 0.769},\n",
        " 'Earth': {'RMSE': 429.335, 'MAE': 321.446, 'R2': 0.556},\n",
-       " 'BaggingRegressor': {'RMSE': 236.5, 'MAE': 144.368, 'R2': 0.865},\n",
-       " 'RandomForestRegressor': {'RMSE': 225.218, 'MAE': 137.182, 'R2': 0.877},\n",
-       " 'GradientBoostingRegressor': {'RMSE': 252.351, 'MAE': 168.389, 'R2': 0.846},\n",
-       " 'XGBRegressor': {'RMSE': 220.127, 'MAE': 136.805, 'R2': 0.883},\n",
+       " 'BaggingRegressor': {'RMSE': 232.851, 'MAE': 142.987, 'R2': 0.869},\n",
+       " 'RandomForestRegressor': {'RMSE': 224.625, 'MAE': 136.939, 'R2': 0.878},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 252.347, 'MAE': 168.352, 'R2': 0.846},\n",
+       " 'XGBRegressor': {'RMSE': 251.638, 'MAE': 168.246, 'R2': 0.847},\n",
        " 'LGBMRegressor': {'RMSE': 213.927, 'MAE': 133.812, 'R2': 0.889},\n",
        " 'CatBoostRegressor': {'RMSE': 211.156, 'MAE': 132.758, 'R2': 0.892}}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -529,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -540,19 +639,19 @@
        " 'Lasso': {'RMSE': 438.698, 'MAE': 327.013, 'R2': 0.54},\n",
        " 'KernelRidge': {'RMSE': 333.564, 'MAE': 227.02, 'R2': 0.734},\n",
        " 'KNeighborsRegressor': {'RMSE': 306.489, 'MAE': 192.451, 'R2': 0.776},\n",
-       " 'MLPRegressor': {'RMSE': 330.816, 'MAE': 224.285, 'R2': 0.739},\n",
+       " 'MLPRegressor': {'RMSE': 328.488, 'MAE': 222.467, 'R2': 0.742},\n",
        " 'SVR': {'RMSE': 535.479, 'MAE': 365.982, 'R2': 0.315},\n",
-       " 'DecisionTreeRegressor': {'RMSE': 324.438, 'MAE': 188.004, 'R2': 0.749},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 320.243, 'MAE': 186.488, 'R2': 0.755},\n",
        " 'Earth': {'RMSE': 436.442, 'MAE': 326.4, 'R2': 0.545},\n",
-       " 'BaggingRegressor': {'RMSE': 252.159, 'MAE': 150.487, 'R2': 0.848},\n",
-       " 'RandomForestRegressor': {'RMSE': 233.533, 'MAE': 140.724, 'R2': 0.87},\n",
-       " 'GradientBoostingRegressor': {'RMSE': 269.631, 'MAE': 181.888, 'R2': 0.826},\n",
-       " 'XGBRegressor': {'RMSE': 225.564, 'MAE': 138.074, 'R2': 0.878},\n",
+       " 'BaggingRegressor': {'RMSE': 248.349, 'MAE': 149.09, 'R2': 0.853},\n",
+       " 'RandomForestRegressor': {'RMSE': 232.604, 'MAE': 140.553, 'R2': 0.871},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 269.575, 'MAE': 181.896, 'R2': 0.826},\n",
+       " 'XGBRegressor': {'RMSE': 270.605, 'MAE': 180.463, 'R2': 0.825},\n",
        " 'LGBMRegressor': {'RMSE': 218.842, 'MAE': 136.099, 'R2': 0.886},\n",
        " 'CatBoostRegressor': {'RMSE': 213.575, 'MAE': 132.441, 'R2': 0.891}}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -570,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -592,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,23 +721,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'LinearRegression': {'RMSE': 206.801, 'MAE': 126.249, 'R2': 0.897},\n",
-       " 'Ridge': {'RMSE': 206.801, 'MAE': 126.249, 'R2': 0.897},\n",
-       " 'LinearSearch': {'RMSE': 208.398, 'MAE': 128.588, 'R2': 0.895},\n",
-       " 'DifferentialEvolution': {'RMSE': 221.618, 'MAE': 143.401, 'R2': 0.881},\n",
-       " 'GeneticAlgorithm': {'RMSE': 207.662, 'MAE': 126.935, 'R2': 0.896},\n",
-       " 'ParticleSwarmOptimization': {'RMSE': 209.612, 'MAE': 129.631, 'R2': 0.894},\n",
-       " 'SimulatedAnnealing': {'RMSE': 218.325, 'MAE': 141.588, 'R2': 0.885},\n",
-       " 'GreedySearch': {'RMSE': 209.581, 'MAE': 128.788, 'R2': 0.894}}"
+       "{'LinearRegression': {'RMSE': 207.055, 'MAE': 127.009, 'R2': 0.896},\n",
+       " 'Ridge': {'RMSE': 207.055, 'MAE': 127.009, 'R2': 0.896},\n",
+       " 'LinearSearch': {'RMSE': 209.604, 'MAE': 130.455, 'R2': 0.894},\n",
+       " 'DifferentialEvolution': {'RMSE': 208.267, 'MAE': 129.598, 'R2': 0.895},\n",
+       " 'GeneticAlgorithm': {'RMSE': 206.959, 'MAE': 127.092, 'R2': 0.897},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 207.033, 'MAE': 127.102, 'R2': 0.896},\n",
+       " 'SimulatedAnnealing': {'RMSE': 223.145, 'MAE': 143.772, 'R2': 0.879},\n",
+       " 'GreedySearch': {'RMSE': 209.942, 'MAE': 129.521, 'R2': 0.893}}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -649,23 +748,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'LinearRegression': {'RMSE': 205.853, 'MAE': 123.723, 'R2': 0.899},\n",
-       " 'Ridge': {'RMSE': 205.853, 'MAE': 123.723, 'R2': 0.899},\n",
-       " 'LinearSearch': {'RMSE': 217.121, 'MAE': 133.136, 'R2': 0.887},\n",
-       " 'DifferentialEvolution': {'RMSE': 218.405, 'MAE': 137.858, 'R2': 0.886},\n",
-       " 'GeneticAlgorithm': {'RMSE': 212.087, 'MAE': 128.353, 'R2': 0.893},\n",
-       " 'ParticleSwarmOptimization': {'RMSE': 205.584, 'MAE': 123.854, 'R2': 0.899},\n",
-       " 'SimulatedAnnealing': {'RMSE': 233.917, 'MAE': 148.864, 'R2': 0.869},\n",
-       " 'GreedySearch': {'RMSE': 215.531, 'MAE': 130.791, 'R2': 0.889}}"
+       "{'LinearRegression': {'RMSE': 206.19, 'MAE': 124.295, 'R2': 0.898},\n",
+       " 'Ridge': {'RMSE': 206.189, 'MAE': 124.295, 'R2': 0.898},\n",
+       " 'LinearSearch': {'RMSE': 221.178, 'MAE': 136.426, 'R2': 0.883},\n",
+       " 'DifferentialEvolution': {'RMSE': 211.44, 'MAE': 128.732, 'R2': 0.893},\n",
+       " 'GeneticAlgorithm': {'RMSE': 206.976, 'MAE': 125.39, 'R2': 0.898},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 206.269, 'MAE': 124.592, 'R2': 0.898},\n",
+       " 'SimulatedAnnealing': {'RMSE': 211.439, 'MAE': 133.123, 'R2': 0.893},\n",
+       " 'GreedySearch': {'RMSE': 215.868, 'MAE': 131.394, 'R2': 0.889}}"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -683,54 +782,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'LinearRegression': {'RMSE': 206.801, 'MAE': 126.249, 'R2': 0.897},\n",
-       " 'Ridge': {'RMSE': 206.801, 'MAE': 126.249, 'R2': 0.897},\n",
-       " 'LinearSearch': {'RMSE': 208.398, 'MAE': 128.588, 'R2': 0.895},\n",
-       " 'DifferentialEvolution': {'RMSE': 209.036, 'MAE': 129.369, 'R2': 0.894},\n",
-       " 'GeneticAlgorithm': {'RMSE': 207.079, 'MAE': 126.384, 'R2': 0.896},\n",
-       " 'ParticleSwarmOptimization': {'RMSE': 206.813, 'MAE': 126.47, 'R2': 0.897},\n",
-       " 'SimulatedAnnealing': {'RMSE': 223.228, 'MAE': 144.525, 'R2': 0.88},\n",
-       " 'GreedySearch': {'RMSE': 209.581, 'MAE': 128.788, 'R2': 0.894}}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "meta_cv_performance"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'LinearRegression': {'RMSE': 205.853, 'MAE': 123.723, 'R2': 0.899},\n",
-       " 'Ridge': {'RMSE': 205.853, 'MAE': 123.723, 'R2': 0.899},\n",
-       " 'LinearSearch': {'RMSE': 217.121, 'MAE': 133.136, 'R2': 0.887},\n",
-       " 'DifferentialEvolution': {'RMSE': 208.173, 'MAE': 125.838, 'R2': 0.896},\n",
-       " 'GeneticAlgorithm': {'RMSE': 207.444, 'MAE': 125.305, 'R2': 0.897},\n",
-       " 'ParticleSwarmOptimization': {'RMSE': 205.842, 'MAE': 123.828, 'R2': 0.899},\n",
-       " 'SimulatedAnnealing': {'RMSE': 216.304, 'MAE': 135.868, 'R2': 0.888},\n",
-       " 'GreedySearch': {'RMSE': 215.531, 'MAE': 130.791, 'R2': 0.889}}"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "meta_test_performance"
    ]
@@ -744,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,39 +843,8 @@
     "    print(num_weights)\n",
     "    print(weights)\n",
     "\n",
-    "    return weights/float(num_weights)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "took 2.8214519023895264 seconds.\n",
-      "100\n",
-      "[ 0  0  0  0  9  0  0  2  0  0 30  0  0 21 38]\n",
-      "took 5.417552709579468 seconds.\n",
-      "187\n",
-      "[ 0  0  0  0 16  0  0  4  0  0 55  0  0 40 72]\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'str'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-42-2bcb9aebfcd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mr2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mmeta_test_performance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GreedyStacking'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'RMSE'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mmeta_test_performance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GreedyStacking'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'MAE'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmae\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mmeta_test_performance\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GreedyStacking'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'R2'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'str'"
-     ]
-    }
-   ],
-   "source": [
+    "    return weights/float(num_weights)\n",
+    "\n",
     "weight1 = greedy_opt(X_train_meta.to_numpy(), y_train_meta.to_numpy(), metric=metric_spearman, converged=conv_manhattan, eps=1e-2)\n",
     "weight2 = greedy_opt(X_train_meta.to_numpy(), y_train_meta.to_numpy(), metric=metric_spearman, converged=conv_euclid, eps=1e-2)\n",
     "\n",
@@ -820,101 +852,966 @@
     "\n",
     "rmse = mean_squared_error(y_test_meta, y_pred, squared=False)\n",
     "mae = mean_absolute_error(y_test_meta, y_pred)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "meta_test_performance['GreedyStacking'.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
-    "meta_test_performance['GreedyStacking'.__class__.__name__]['MAE'] = round(mae, 3)\n",
-    "meta_test_performance['GreedyStacking'.__class__.__name__]['R2'] = round(r2, 3)"
+    "r2 = r2_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ===================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection (SKIPPED!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameter Tuning (SKIPPED!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pre-process Slump Test Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
-    "meta_test_performance"
+    "slump = pd.read_csv(path + \"/data/slump_test.data\", header=0, encoding= 'unicode_escape')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 108,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "215.74350006437393"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "slump.rename(columns={'Fly ash': 'FlyAsh', 'Coarse Aggr.': 'CoarseAggr', 'Fine Aggr.': 'FineAggr', 'SLUMP(cm)': 'Slump' , 'FLOW(cm)' : 'Flow' , 'Compressive Strength (28-day)(Mpa)': 'CompressiveStr'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slump.drop(columns=['No'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "rmse"
+    "#slump.drop(slump.tail(1).index,inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "131.55150688629558"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 103 entries, 0 to 102\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column          Non-Null Count  Dtype  \n",
+      "---  ------          --------------  -----  \n",
+      " 0   Cement          103 non-null    float64\n",
+      " 1   Slag            103 non-null    float64\n",
+      " 2   FlyAsh          103 non-null    float64\n",
+      " 3   Water           103 non-null    float64\n",
+      " 4   SP              103 non-null    float64\n",
+      " 5   CoarseAggr      103 non-null    float64\n",
+      " 6   FineAggr        103 non-null    float64\n",
+      " 7   Slump           103 non-null    float64\n",
+      " 8   Flow            103 non-null    float64\n",
+      " 9   CompressiveStr  103 non-null    float64\n",
+      "dtypes: float64(10)\n",
+      "memory usage: 8.2 KB\n"
+     ]
     }
    ],
    "source": [
-    "mae"
+    "slump.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Cement</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>229.894175</td>\n",
+       "      <td>78.877230</td>\n",
+       "      <td>137.00</td>\n",
+       "      <td>152.00</td>\n",
+       "      <td>248.00</td>\n",
+       "      <td>303.900</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slag</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>77.973786</td>\n",
+       "      <td>60.461363</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>125.000</td>\n",
+       "      <td>193.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FlyAsh</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>149.014563</td>\n",
+       "      <td>85.418080</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>115.50</td>\n",
+       "      <td>164.00</td>\n",
+       "      <td>235.950</td>\n",
+       "      <td>260.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Water</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>197.167961</td>\n",
+       "      <td>20.208158</td>\n",
+       "      <td>160.00</td>\n",
+       "      <td>180.00</td>\n",
+       "      <td>196.00</td>\n",
+       "      <td>209.500</td>\n",
+       "      <td>240.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SP</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>8.539806</td>\n",
+       "      <td>2.807530</td>\n",
+       "      <td>4.40</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>10.000</td>\n",
+       "      <td>19.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CoarseAggr</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>883.978641</td>\n",
+       "      <td>88.391393</td>\n",
+       "      <td>708.00</td>\n",
+       "      <td>819.50</td>\n",
+       "      <td>879.00</td>\n",
+       "      <td>952.800</td>\n",
+       "      <td>1049.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FineAggr</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>739.604854</td>\n",
+       "      <td>63.342117</td>\n",
+       "      <td>640.60</td>\n",
+       "      <td>684.50</td>\n",
+       "      <td>742.70</td>\n",
+       "      <td>788.000</td>\n",
+       "      <td>902.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slump</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>18.048544</td>\n",
+       "      <td>8.750844</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>14.50</td>\n",
+       "      <td>21.50</td>\n",
+       "      <td>24.000</td>\n",
+       "      <td>29.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Flow</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>49.610680</td>\n",
+       "      <td>17.568610</td>\n",
+       "      <td>20.00</td>\n",
+       "      <td>38.50</td>\n",
+       "      <td>54.00</td>\n",
+       "      <td>63.750</td>\n",
+       "      <td>78.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CompressiveStr</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>36.039417</td>\n",
+       "      <td>7.838232</td>\n",
+       "      <td>17.19</td>\n",
+       "      <td>30.90</td>\n",
+       "      <td>35.52</td>\n",
+       "      <td>41.205</td>\n",
+       "      <td>58.53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "0.8888279162237089"
+       "                count        mean        std     min     25%     50%      75%  \\\n",
+       "Cement          103.0  229.894175  78.877230  137.00  152.00  248.00  303.900   \n",
+       "Slag            103.0   77.973786  60.461363    0.00    0.05  100.00  125.000   \n",
+       "FlyAsh          103.0  149.014563  85.418080    0.00  115.50  164.00  235.950   \n",
+       "Water           103.0  197.167961  20.208158  160.00  180.00  196.00  209.500   \n",
+       "SP              103.0    8.539806   2.807530    4.40    6.00    8.00   10.000   \n",
+       "CoarseAggr      103.0  883.978641  88.391393  708.00  819.50  879.00  952.800   \n",
+       "FineAggr        103.0  739.604854  63.342117  640.60  684.50  742.70  788.000   \n",
+       "Slump           103.0   18.048544   8.750844    0.00   14.50   21.50   24.000   \n",
+       "Flow            103.0   49.610680  17.568610   20.00   38.50   54.00   63.750   \n",
+       "CompressiveStr  103.0   36.039417   7.838232   17.19   30.90   35.52   41.205   \n",
+       "\n",
+       "                    max  \n",
+       "Cement           374.00  \n",
+       "Slag             193.00  \n",
+       "FlyAsh           260.00  \n",
+       "Water            240.00  \n",
+       "SP                19.00  \n",
+       "CoarseAggr      1049.90  \n",
+       "FineAggr         902.00  \n",
+       "Slump             29.00  \n",
+       "Flow              78.00  \n",
+       "CompressiveStr    58.53  "
       ]
      },
-     "execution_count": 45,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "r2"
+    "slump.describe().transpose()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 122,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# ===================================================================="
+    "X = slump[['Cement','Slag','FlyAsh','Water','SP','CoarseAggr','FineAggr']]\n",
+    "y = slump[['Slump']]\n",
+    "#y = slump[['Slump','Flow','CompressiveStr']]"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 139,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Feature Selection (SKIPPED!)"
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=333)\n",
+    "base_train_prediction = {}\n",
+    "base_train_prediction['target'] = y_train.to_numpy().ravel()\n",
+    "base_test_prediction = {}\n",
+    "base_test_prediction['target'] = y_test.to_numpy().ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_cv_performance = {}\n",
+    "base_test_performance = {}\n",
+    "meta_cv_performance = {}\n",
+    "meta_test_performance = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:16:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in base_models:\n",
+    "    model = base_models[m]\n",
+    "    base_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    prediction, performance = base_cv_learning(model, X_train, y_train, 5)\n",
+    "    base_train_prediction[model.__class__.__name__] = prediction\n",
+    "    base_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    try:\n",
+    "        base_test_prediction[model.__class__.__name__] = [items[0] for items in y_pred.tolist()]\n",
+    "    except:\n",
+    "        base_test_prediction[model.__class__.__name__] = y_pred.tolist()\n",
+    "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    base_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 7.934, 'MAE': 6.498, 'R2': -0.571},\n",
+       " 'Ridge': {'RMSE': 7.934, 'MAE': 6.497, 'R2': -0.57},\n",
+       " 'Lasso': {'RMSE': 7.804, 'MAE': 6.401, 'R2': -0.523},\n",
+       " 'KernelRidge': {'RMSE': 20.051, 'MAE': 18.283, 'R2': -16.7},\n",
+       " 'KNeighborsRegressor': {'RMSE': 8.001, 'MAE': 6.033, 'R2': -0.255},\n",
+       " 'MLPRegressor': {'RMSE': 35.985, 'MAE': 32.608, 'R2': -41.441},\n",
+       " 'SVR': {'RMSE': 8.723, 'MAE': 6.494, 'R2': -0.381},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 9.61, 'MAE': 6.629, 'R2': -1.828},\n",
+       " 'BaggingRegressor': {'RMSE': 7.598, 'MAE': 5.294, 'R2': -0.53},\n",
+       " 'RandomForestRegressor': {'RMSE': 7.217, 'MAE': 5.285, 'R2': -0.262},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 8.402, 'MAE': 6.041, 'R2': -0.951},\n",
+       " 'XGBRegressor': {'RMSE': 7.971, 'MAE': 5.678, 'R2': -0.463},\n",
+       " 'LGBMRegressor': {'RMSE': 7.534, 'MAE': 5.947, 'R2': -0.194},\n",
+       " 'CatBoostRegressor': {'RMSE': 7.55, 'MAE': 5.446, 'R2': -0.341}}"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 9.095, 'MAE': 7.076, 'R2': -0.021},\n",
+       " 'Ridge': {'RMSE': 9.094, 'MAE': 7.076, 'R2': -0.021},\n",
+       " 'Lasso': {'RMSE': 8.987, 'MAE': 7.067, 'R2': 0.003},\n",
+       " 'KernelRidge': {'RMSE': 19.151, 'MAE': 16.904, 'R2': -3.526},\n",
+       " 'KNeighborsRegressor': {'RMSE': 11.173, 'MAE': 8.326, 'R2': -0.54},\n",
+       " 'MLPRegressor': {'RMSE': 59.419, 'MAE': 57.623, 'R2': -42.567},\n",
+       " 'SVR': {'RMSE': 10.264, 'MAE': 6.858, 'R2': -0.3},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 11.013, 'MAE': 7.81, 'R2': -0.497},\n",
+       " 'BaggingRegressor': {'RMSE': 9.079, 'MAE': 6.569, 'R2': -0.017},\n",
+       " 'RandomForestRegressor': {'RMSE': 8.396, 'MAE': 6.231, 'R2': 0.13},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 9.788, 'MAE': 6.808, 'R2': -0.182},\n",
+       " 'XGBRegressor': {'RMSE': 9.837, 'MAE': 6.596, 'R2': -0.194},\n",
+       " 'LGBMRegressor': {'RMSE': 8.981, 'MAE': 6.878, 'R2': 0.005},\n",
+       " 'CatBoostRegressor': {'RMSE': 9.116, 'MAE': 6.394, 'R2': -0.025}}"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_test_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train_meta = pd.DataFrame(base_train_prediction)\n",
+    "y_train_meta = df_train_meta[['target']]\n",
+    "X_train_meta = df_train_meta.drop(columns=['target'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_meta = pd.DataFrame(base_test_prediction)\n",
+    "y_test_meta = df_test_meta[['target']].to_numpy().ravel()\n",
+    "X_test_meta = df_test_meta.drop(columns=['target'], axis=1).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for m in meta_models:\n",
+    "    model = meta_models[m]\n",
+    "    meta_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    performance = meta_cv_learning(model, X_train_meta, y_train_meta, 5)\n",
+    "    meta_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train_meta.to_numpy(), y_train_meta.to_numpy().ravel())\n",
+    "    y_pred = model.predict(X_test_meta)\n",
+    "    rmse = mean_squared_error(y_test_meta, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test_meta, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    meta_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 9.061, 'MAE': 6.883, 'R2': -1.137},\n",
+       " 'Ridge': {'RMSE': 8.699, 'MAE': 6.673, 'R2': -1.001},\n",
+       " 'LinearSearch': {'RMSE': 7.791, 'MAE': 6.182, 'R2': -0.572},\n",
+       " 'DifferentialEvolution': {'RMSE': 8.526, 'MAE': 6.448, 'R2': -1.069},\n",
+       " 'GeneticAlgorithm': {'RMSE': 8.592, 'MAE': 6.457, 'R2': -1.078},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 8.613, 'MAE': 6.566, 'R2': -1.126},\n",
+       " 'SimulatedAnnealing': {'RMSE': 8.233, 'MAE': 6.421, 'R2': -0.728},\n",
+       " 'GreedySearch': {'RMSE': 15.106, 'MAE': 14.0, 'R2': -10.286}}"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 8.215, 'MAE': 7.061, 'R2': 0.167},\n",
+       " 'Ridge': {'RMSE': 8.018, 'MAE': 6.87, 'R2': 0.207},\n",
+       " 'LinearSearch': {'RMSE': 8.31, 'MAE': 6.35, 'R2': 0.148},\n",
+       " 'DifferentialEvolution': {'RMSE': 8.348, 'MAE': 6.838, 'R2': 0.14},\n",
+       " 'GeneticAlgorithm': {'RMSE': 8.294, 'MAE': 6.931, 'R2': 0.151},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 8.386, 'MAE': 6.979, 'R2': 0.132},\n",
+       " 'SimulatedAnnealing': {'RMSE': 8.323, 'MAE': 6.634, 'R2': 0.145},\n",
+       " 'GreedySearch': {'RMSE': 14.201, 'MAE': 12.813, 'R2': -1.489}}"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_test_performance"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Parameter Tuning (SKIPPED!)"
+    "# Pre-process Slump Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock = pd.read_csv(path + \"/data/Stock Portofolio.csv\", header=0, encoding= 'unicode_escape')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock.rename(columns=dict(zip(stock.columns, ['largeBP', 'largeROE', 'largeSP', 'largeReturn', 'largeValue', 'smallRisk', 'Return'])), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>largeBP</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeROE</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeSP</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeReturn</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>largeValue</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>smallRisk</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.166619</td>\n",
+       "      <td>0.199304</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>0.2915</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Return</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>14.923810</td>\n",
+       "      <td>2.787224</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>15.300</td>\n",
+       "      <td>17.0000</td>\n",
+       "      <td>19.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count       mean       std  min   25%     50%      75%   max\n",
+       "largeBP       63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeROE      63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeSP       63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeReturn   63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "largeValue    63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "smallRisk     63.0   0.166619  0.199304  0.0   0.0   0.167   0.2915   1.0\n",
+       "Return        63.0  14.923810  2.787224  7.0  13.8  15.300  17.0000  19.5"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 63 entries, 0 to 62\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   largeBP      63 non-null     float64\n",
+      " 1   largeROE     63 non-null     float64\n",
+      " 2   largeSP      63 non-null     float64\n",
+      " 3   largeReturn  63 non-null     float64\n",
+      " 4   largeValue   63 non-null     float64\n",
+      " 5   smallRisk    63 non-null     float64\n",
+      " 6   Return       63 non-null     float64\n",
+      "dtypes: float64(7)\n",
+      "memory usage: 3.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "stock.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = stock[['largeBP','largeROE','largeSP','largeReturn','largeValue','smallRisk']]\n",
+    "y = stock[['Return']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=333)\n",
+    "base_train_prediction = {}\n",
+    "base_train_prediction['target'] = y_train.to_numpy().ravel()\n",
+    "base_test_prediction = {}\n",
+    "base_test_prediction['target'] = y_test.to_numpy().ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_cv_performance = {}\n",
+    "base_test_performance = {}\n",
+    "meta_cv_performance = {}\n",
+    "meta_test_performance = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      "[00:24:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in base_models:\n",
+    "    model = base_models[m]\n",
+    "    base_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    prediction, performance = base_cv_learning(model, X_train, y_train, 5)\n",
+    "    base_train_prediction[model.__class__.__name__] = prediction\n",
+    "    base_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    try:\n",
+    "        base_test_prediction[model.__class__.__name__] = [items[0] for items in y_pred.tolist()]\n",
+    "    except:\n",
+    "        base_test_prediction[model.__class__.__name__] = y_pred.tolist()\n",
+    "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    base_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    base_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 2.282, 'MAE': 1.874, 'R2': 0.272},\n",
+       " 'Ridge': {'RMSE': 2.12, 'MAE': 1.782, 'R2': 0.381},\n",
+       " 'Lasso': {'RMSE': 2.728, 'MAE': 2.147, 'R2': -0.025},\n",
+       " 'KernelRidge': {'RMSE': 2.056, 'MAE': 1.694, 'R2': 0.424},\n",
+       " 'KNeighborsRegressor': {'RMSE': 1.959, 'MAE': 1.432, 'R2': 0.463},\n",
+       " 'MLPRegressor': {'RMSE': 1.895, 'MAE': 1.629, 'R2': 0.489},\n",
+       " 'SVR': {'RMSE': 1.888, 'MAE': 1.342, 'R2': 0.51},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 1.73, 'MAE': 1.498, 'R2': 0.585},\n",
+       " 'BaggingRegressor': {'RMSE': 1.861, 'MAE': 1.453, 'R2': 0.525},\n",
+       " 'RandomForestRegressor': {'RMSE': 1.655, 'MAE': 1.332, 'R2': 0.62},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 1.163, 'MAE': 0.963, 'R2': 0.805},\n",
+       " 'XGBRegressor': {'RMSE': 1.559, 'MAE': 1.174, 'R2': 0.664},\n",
+       " 'LGBMRegressor': {'RMSE': 2.699, 'MAE': 2.114, 'R2': -0.04},\n",
+       " 'CatBoostRegressor': {'RMSE': 1.545, 'MAE': 1.172, 'R2': 0.672}}"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 1.49, 'MAE': 1.267, 'R2': 0.719},\n",
+       " 'Ridge': {'RMSE': 1.842, 'MAE': 1.611, 'R2': 0.571},\n",
+       " 'Lasso': {'RMSE': 2.879, 'MAE': 2.307, 'R2': -0.049},\n",
+       " 'KernelRidge': {'RMSE': 2.119, 'MAE': 1.723, 'R2': 0.432},\n",
+       " 'KNeighborsRegressor': {'RMSE': 1.751, 'MAE': 1.489, 'R2': 0.612},\n",
+       " 'MLPRegressor': {'RMSE': 1.572, 'MAE': 1.385, 'R2': 0.688},\n",
+       " 'SVR': {'RMSE': 1.822, 'MAE': 1.382, 'R2': 0.58},\n",
+       " 'DecisionTreeRegressor': {'RMSE': 2.066, 'MAE': 1.777, 'R2': 0.46},\n",
+       " 'BaggingRegressor': {'RMSE': 1.705, 'MAE': 1.414, 'R2': 0.632},\n",
+       " 'RandomForestRegressor': {'RMSE': 1.724, 'MAE': 1.489, 'R2': 0.624},\n",
+       " 'GradientBoostingRegressor': {'RMSE': 1.106, 'MAE': 0.897, 'R2': 0.845},\n",
+       " 'XGBRegressor': {'RMSE': 1.271, 'MAE': 1.006, 'R2': 0.796},\n",
+       " 'LGBMRegressor': {'RMSE': 1.505, 'MAE': 1.121, 'R2': 0.714},\n",
+       " 'CatBoostRegressor': {'RMSE': 1.54, 'MAE': 1.25, 'R2': 0.7}}"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_test_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train_meta = pd.DataFrame(base_train_prediction)\n",
+    "y_train_meta = df_train_meta[['target']]\n",
+    "X_train_meta = df_train_meta.drop(columns=['target'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_meta = pd.DataFrame(base_test_prediction)\n",
+    "y_test_meta = df_test_meta[['target']].to_numpy().ravel()\n",
+    "X_test_meta = df_test_meta.drop(columns=['target'], axis=1).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for m in meta_models:\n",
+    "    model = meta_models[m]\n",
+    "    meta_test_performance[model.__class__.__name__] = {}\n",
+    "    \n",
+    "    performance = meta_cv_learning(model, X_train_meta, y_train_meta, 5)\n",
+    "    meta_cv_performance[model.__class__.__name__] = performance\n",
+    "    \n",
+    "    model.fit(X_train_meta.to_numpy(), y_train_meta.to_numpy().ravel())\n",
+    "    y_pred = model.predict(X_test_meta)\n",
+    "    rmse = mean_squared_error(y_test_meta, y_pred, squared=False)\n",
+    "    mae = mean_absolute_error(y_test_meta, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    meta_test_performance[model.__class__.__name__]['RMSE'] = round(rmse, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['MAE'] = round(mae, 3)\n",
+    "    meta_test_performance[model.__class__.__name__]['R2'] = round(r2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 1.558, 'MAE': 1.29, 'R2': 0.66},\n",
+       " 'Ridge': {'RMSE': 1.315, 'MAE': 1.096, 'R2': 0.758},\n",
+       " 'LinearSearch': {'RMSE': 1.365, 'MAE': 1.123, 'R2': 0.732},\n",
+       " 'DifferentialEvolution': {'RMSE': 1.473, 'MAE': 1.196, 'R2': 0.686},\n",
+       " 'GeneticAlgorithm': {'RMSE': 1.164, 'MAE': 0.981, 'R2': 0.814},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 1.326, 'MAE': 1.078, 'R2': 0.754},\n",
+       " 'SimulatedAnnealing': {'RMSE': 1.175, 'MAE': 0.981, 'R2': 0.804},\n",
+       " 'GreedySearch': {'RMSE': 2.076, 'MAE': 1.606, 'R2': 0.413}}"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_cv_performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LinearRegression': {'RMSE': 1.214, 'MAE': 1.025, 'R2': 0.814},\n",
+       " 'Ridge': {'RMSE': 1.123, 'MAE': 0.896, 'R2': 0.84},\n",
+       " 'LinearSearch': {'RMSE': 1.231, 'MAE': 0.992, 'R2': 0.809},\n",
+       " 'DifferentialEvolution': {'RMSE': 1.291, 'MAE': 1.02, 'R2': 0.789},\n",
+       " 'GeneticAlgorithm': {'RMSE': 1.057, 'MAE': 0.857, 'R2': 0.859},\n",
+       " 'ParticleSwarmOptimization': {'RMSE': 1.138, 'MAE': 0.906, 'R2': 0.836},\n",
+       " 'SimulatedAnnealing': {'RMSE': 1.477, 'MAE': 1.264, 'R2': 0.724},\n",
+       " 'GreedySearch': {'RMSE': 2.14, 'MAE': 1.797, 'R2': 0.421}}"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_test_performance"
    ]
   },
   {
diff --git a/src/__pycache__/skoptimize.cpython-37.pyc b/src/__pycache__/skoptimize.cpython-37.pyc
index defebee..a29edb7 100644
Binary files a/src/__pycache__/skoptimize.cpython-37.pyc and b/src/__pycache__/skoptimize.cpython-37.pyc differ
diff --git a/src/catboost_info/catboost_training.json b/src/catboost_info/catboost_training.json
index 1fc65c4..1812b46 100644
--- a/src/catboost_info/catboost_training.json
+++ b/src/catboost_info/catboost_training.json
@@ -1,1004 +1,1004 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":1000,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
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diff --git a/src/catboost_info/learn/events.out.tfevents b/src/catboost_info/learn/events.out.tfevents
index fa04773..852cb99 100644
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diff --git a/src/catboost_info/learn_error.tsv b/src/catboost_info/learn_error.tsv
index 630912a..7084c5e 100644
--- a/src/catboost_info/learn_error.tsv
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diff --git a/src/catboost_info/time_left.tsv b/src/catboost_info/time_left.tsv
index be1fa19..c61046c 100644
--- a/src/catboost_info/time_left.tsv
+++ b/src/catboost_info/time_left.tsv
@@ -1,1001 +1,1001 @@
 iter	Passed	Remaining
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