'max_depth': [3, 4, 5, 6]
}
gabooc_grid = GridSearchCV(GaBooC, param_grid=gabooc_param, cv=10)
gabooc_scores = gabooc_grid.fit(X_train_scaled, Y_train_scaled)
feature_importance_plot(gabooc_grid, X_train_scaled.columns)
# Extreme Gradient Boosting
xgb = XGBClassifier(random_state=0)
xgb_param = {
'max_depth': [5, 6, 7, 8],
'gamma': [1, 2, 3, 4],
'learning_rate': [0.1, 0.2, 0.3, 0.5]
}
xgb_grid = GridSearchCV(xgb, param_grid=xgb_param, cv=10)
xgb.scores = xgb_grid.fit(X_train_scaled, Y_train_scaled)
feature_importance_plot(xgb_grid, X_train_scaled.columns)
print(xgb.scores.best_score_)
#AdaBoost
ada = AdaBoostClassifier(random_state=0)
ada_param = {'n_estimators': [30, 50, 100], 'learning_rate': [0.001, 0.1, 1]}
ada_grid = GridSearchCV(ada, param_grid=ada_param, cv=10)
ada_scores = ada_grid.fit(X_train_scaled, Y_train_scaled)
print(ada_scores.best_score_)
feature_importance_plot(ada_grid, X_train_scaled.columns)
#CatBoost
cat = CatBoostClassifier(random_state=0)
cat_param = {
'iterations': [100, 150],
