不同的结果scikit学习wapper

Question

这里例如，对于姬松茸的样本数据：

import xgboost as xgb 
from sklearn.datasets import load_svmlight_files 

X_train, y_train, X_test, y_test = load_svmlight_files(('agaricus.txt.train', 'agaricus.txt.test')) 

clf = xgb.XGBClassifier() 
param = clf.get_xgb_params() 
clf.fit(X_train, y_train) 
preds_sk = clf.predict_proba(X_test) 

dtrain = xgb.DMatrix(X_train, label=y_train) 
dtest = xgb.DMatrix(X_test) 
bst = xgb.train(param, dtrain) 
preds = bst.predict(dtest) 

print preds_sk 
print preds 

而且结果是：

[[ 9.98860419e-01 1.13956432e-03] 
[ 2.97790766e-03 9.97022092e-01] 
[ 9.98816252e-01 1.18372787e-03] 
..., 
[ 1.95205212e-04 9.99804795e-01] 
[ 9.98845220e-01 1.15479471e-03] 
[ 5.69522381e-04 9.99430478e-01]] 

[ 0.21558253 0.7351886 0.21558253 ..., 0.81527805 0.18158565 
    0.81527805] 

为何结果不同？似乎所有的默认参数值都是相同的。我在这里并不是说predict_proba返回[prob, 1- prob]。

xgboost V0.6，scikit学习v0.18.1，蟒蛇2.7.12

Answer 1

您需要直接传递num_boost_round参数xgb.train：

bst = xgb.train(param, dtrain,num_boost_round=param['n_estimators']) 

，否则它忽略PARAM [” n_estimators']并使用默认数量的估计器，对于xgb.train接口，当前为10，而对于n_estimators，默认值为100.