不同的結果scikit學習wapper

Question

這裏例如，對於姬松茸的樣本數據：

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 

而且結果是：

[[ 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] 

爲何結果不同？似乎所有的默認參數值都是相同的。我在這裏並不是說predict_proba返回[prob, 1- prob]。

xgboost V0.6，scikit學習v0.18.1，蟒蛇2.7.12

Answer 1

您需要直接傳遞num_boost_round參數xgb.train：

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

，否則它忽略PARAM [」 n_estimators']並使用默認數量的估計器，對於xgb.train接口，當前爲10，而對於n_estimators，默認值爲100.