clf = XGBClassifier(learning_rate = 0.01, n_estimators = 5000, reg_alpha = 0.025, colsample_bytree = 0.8, silent = 1, scale_pos_weight = 0, nthread = 4, min_child_weight = 1, subsample= 0.8, seed = 1337, objective= 'multi:softprob', max_depth = 7, gamma= .2) # use the xgb interface xgb_param = clf.get_xgb_params() xgb_param['num_class'] = 5 xgb_param['eval_metric'] = 'mlogloss' Xg_train = xgb.DMatrix(X_train, label=y_train, missing=np.nan) cvresult = xgb.cv(xgb_param, Xg_train, num_boost_round = clf.get_params()['n_estimators'], nfold = 5, show_progress = True, early_stopping_rounds = 100) clf.set_params(n_estimators=cvresult.shape[0]) clf.fit(X_train, y_train) best_outcome_params = clf.get_params() best_outcome_score = cvresult.min() try:
