Machine Learning Models Made Simple
classifier->
Scaling-> [train data, test data]
standard_scaler
MinMax
TrainSplitTest-> [train data, test data, test_size,random_state]
split
classifierSingleModel-> [X_train,y_train,X_test,y_test]
model_fit
classifierHyper-> [x_train,x_test,y_train,y_test]
adaBoost, ada_pred
randomforest, rf_pred
gradientboost, gb_pred
logistic, l_pred
svm, svm_pred
knn, knn_pred
binomialnb, nb_pred
multinomialnb, multinomialnb_pred
gaussiannb, gaussiannb_pred
decisiontree, decisiontree_pred
xgboost, xgboost_pred
bagging, bagging_pred
extra, extra_pred
ridge, ridge_pred
Modelling-> [x_train, x_test, y_train, y_test]
logistic, log_pred
k_nn, knn_pred
svc, svc_pred
decision_tree, dt_pred
random_forest, rf_pred
gradient_boosting, gb_pred
XGBOOST, xgb_pred
adaBoostC, abc_pred
bernoullinb, bnb_pred
multinomialnb, mnb_pred
bagging, bagging_pred
extraTrees, et_pred
ridge, r_pred
sgd, sgd_pred
regression->
regressionSingleModel-> [X_train,y_train,X_test,y_test]
model_fit
RegressionHyper-> [x_train,y_train,x_test,y_test]
lasso, lasso_pred
ridge, ridge_pred
elastic, elastic_pred
svr, svr_pred
knn, knn_pred
rf, rf_pred
gb, gb_pred
lr, lr_pred
xgboost, xg_pred
sgdregressor, sgd_pred
decisiontree, dt_pred
ada_boost, ada_pred
theilsenregressor, theil_pred
ransacregressor, ransac_pred
orthogonalmatchingpursuit, ortho_pred
lassolars, lasso_pred
lars, lars_pred
huberregresso,r huber_pred
passiveaggressiveregressor, passiveaggressive_pred
ardregression, ard_pred
bayesianridge, bayesianridge_pred
baggingregressor, bagging_pred
extratreesregressor, extratrees_pred