gbdt和xgboost api

class xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective=‘reg:linear‘, booster=‘gbtree‘, n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

class sklearn.ensemble.GradientBoostingClassifier(loss=‘deviance‘, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=‘friedman_mse‘, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_split=1e-07, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort=‘auto‘)

原文：http://www.cnblogs.com/wuxiangli/p/7239026.html