param_test1 = {

 'max_depth':range(3,10,2),

 'min_child_weight':range(1,6,2)

}

gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5,

 min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,

 objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), 

 param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)

gsearch1.fit(train[predictors],train[target])

gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_

param_test2 = {

 'max_depth':[4,5,6],

 'min_child_weight':[4,5,6]

}

gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=5,

 min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8,

 objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), 

 param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)

gsearch2.fit(train[predictors],train[target])

gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_

param_test6 = {

 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]

}

gsearch6 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4,

 min_child_weight=6, gamma=0.1, subsample=0.8, colsample_bytree=0.8,

 objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), 

 param_grid = param_test6, scoring='roc_auc',n_jobs=4,iid=False, cv=5)

gsearch6.fit(train[predictors],train[target])

gsearch6.grid_scores_, gsearch6.best_params_, gsearch6.best_score_