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13_Grid_search
from sklearn.model_selection import GridSearchCV
X_train, X_test, y_train, y_test = train_test_split(X_sm, y_sm, test_size=0.2, random_state=42)
#----------------------------------------------------------------logistic regression classifier
#define hyper parameters and ranges
param_grid_log = [{'C': [0.1, 1, 10], 'solver': ['lbfgs', 'liblinear'],
'max_iter':[100, 300]}]
#apply gridsearch
grid_log = GridSearchCV(log, param_grid=param_grid_log, cv=5)
#fit model with grid search
grid_log.fit(X_train, y_train)
print('The best parameters for log classifier: ', grid_log.best_params_)
#----------------------------------------------------------------kNN classifier
#define hyper parameters and ranges
#define hyper parameters and ranges
param_grid_knn = [{'n_neighbors': [2, 3, 4, 6, 8, 10], 'weights': [ 'uniform', 'distance'],
'metric': ['euclidean', 'manhattan', 'minkowski']}]
#apply gridsearch
grid_knn = GridSearchCV(knn, param_grid=param_grid_knn, cv=5)
#fit model with grid search
grid_knn.fit(X_train, y_train)
print('The best parameters for knn classifier: ', grid_knn.best_params_)
#--------------------------------------------------------------decision tree classifier
#define hyper parameters and ranges
param_grid_dtree = [{'max_depth': [ 15, 20, 25, 30], 'criterion': ['gini', 'entropy']}]
#apply gridsearch
grid_dtree = GridSearchCV(dtree, param_grid=param_grid_dtree, cv=5)
#fit model with grid search
grid_dtree.fit(X_train, y_train)
print('The best parameters for dtree classifier: ', grid_dtree.best_params_)
#--------------------------------------------------------------random forest classifier
#define hyper parameters and ranges
param_grid_rtree = [{'max_depth': [5, 10, 15, 20], 'n_estimators':[100,300,500] ,
'criterion': ['gini', 'entropy']}]
#apply gridsearch
grid_rtree = GridSearchCV(rtree, param_grid=param_grid_rtree, cv=5)
#fit model with grid search
grid_rtree.fit(X_train, y_train)
print('The best parameters for rtree classifier: ', grid_rtree.best_params_)
#----------------------------------------------------------------SVM classifier
#define hyper parameters and ranges
param_grid_svm = [{'C': [100, 50, 10, 1.0, 0.1, 0.01], 'gamma': ['scale'],
'kernel': ['poly', 'rbf', 'sigmoid'] }]
#apply gridsearch
grid_svm = GridSearchCV(svm, param_grid=param_grid_svm, cv=5)
#fit model with grid search
grid_svm.fit(X_train, y_train)
print('The best parameters for svm classifier: ', grid_svm.best_params_)
#-----------------------------------------------------------------gbc classifier
#define hyper parameters and ranges
param_grid_gbc = [{'learning_rate': [0.1, 1], 'n_estimators':[200,350,500]}]
#apply gridsearch
grid_gbc = GridSearchCV(gbc, param_grid=param_grid_gbc, cv=5)
#fit model with grid search
grid_gbc.fit(X_train, y_train)
print('The best parameters for gbc classifier: ', grid_gbc.best_params_)
#--------------------------------------------------------------extra tree classifier
#etree classifier
#define hyper parameters and ranges
param_grid_etree = [{'max_depth': [15, 20, 25, 30, 35], 'n_estimators':[200,350,500] ,
'criterion': ['gini', 'entropy']}]
#apply gridsearch
grid_etree = GridSearchCV(etree, param_grid=param_grid_etree, cv=5)
#fit model with grid search
grid_etree.fit(X_train, y_train)
print('The best parameters for etree classifier: ', grid_etree.best_params_)
# The best parameters for log classifier: {'C': 10, 'max_iter': 200, 'solver': 'lbfgs'}
# The best parameters for knn classifier: {'metric': 'manhattan', 'n_neighbors': 2, 'weights': 'distance'}
# The best parameters for dtree classifier: {'criterion': 'entropy', 'max_depth': 20}
# The best parameters for rtree classifier: {'criterion': 'entropy', 'max_depth': 20, 'n_estimators': 500}
# The best parameters for svm classifier: {'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}
#The best parameters for gbc classifier: {'learning_rate': 0.1, 'n_estimators': 500}
#The best parameters for extra tree classifier: {'criterion': 'gini', 'max_depth': 35, 'n_estimators': 350}
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