n_estimators=100, objective='binary:logistic', base_score=0.5, colsample_bylevel=1, gamma=0, colsample_bytree=1, max_delta_step=0, min_child_weight=1, missing=None, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, subsample=1) model.feature_names = feature_names print(model) model.fit(X_train, np.ravel(Y_train)) #save model model_save_name = save_directory + '/antgc_' + signal + '_bdt' model._Booster.dump_model(model_save_name + '.xgb') model._Booster.save_model(model_save_name + '_bin.xgb') pk.dump(model, open(model_save_name + '.pickle', 'wb')) print 'Saved model ' + model_save_name + '(*.xgb, *.pickle)' # save test and test sets train_save_file = save_directory + '/train_set' + signal + '.txt' test_save_file = save_directory + '/test_set' + signal + '.txt' train_save = np.append(X_train, Y_train, axis=1)