added XGBRegressor in machine learning

machine_learning/XGBRegressor/XGBRegressor.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import xgboost as xgb
+from sklearn.datasets import load_boston
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_val_score, KFold
+from sklearn.metrics import mean_squared_error
+import matplotlib.pyplot as plt 
+
+
+# In[3]:
+
+
+boston = load_boston()
+x, y = boston.data, boston.target
+xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15)
+
+
+# In[5]:
+
+
+from xgboost import XGBRegressor
+xgbr = xgb.XGBRegressor(verbosity=0) 
+print(xgbr)
+XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
+       colsample_bynode=1, colsample_bytree=1, gamma=0,
+       importance_type='gain', learning_rate=0.1, max_delta_step=0,
+       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
+       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
+       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
+       silent=None, subsample=1, verbosity=1)
+
+
+# In[6]:
+
+
+xgbr.fit(xtrain, ytrain)
+
+
+# In[8]:
+
+
+score = xgbr.score(xtrain, ytrain)  
+print("Training score: ", score)
+
+
+# In[12]:
+
+
+scores = cross_val_score(xgbr, xtrain, ytrain,cv=10)
+print("Mean cross-validation score: %.2f" % scores.mean())
+
+
+# In[13]:
+
+
+kfold = KFold(n_splits=10, shuffle=True)
+kf_cv_scores = cross_val_score(xgbr, xtrain, ytrain, cv=kfold )
+print("K-fold CV average score: %.2f" % kf_cv_scores.mean())
+
+
+# In[14]:
+
+
+ypred = xgbr.predict(xtest)
+mse = mean_squared_error(ytest, ypred)
+print("MSE: %.2f" % mse)
+MSE: 3.35
+print("RMSE: %.2f" % (mse**(1/2.0)))
+
+
+# In[15]:
+
+
+x_ax = range(len(ytest))
+plt.plot(x_ax, ytest, label="original")
+plt.plot(x_ax, ypred, label="predicted")
+plt.title("Boston test and predicted data")
+plt.legend()
+plt.show()
+
+
+# In[ ]:
+
+
+
+