XGBClassifier上的交叉验证,用于python中的多类分类

概述我正在尝试使用从http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/改编的以下代码在XGBC分类器上执行多类分类问题的交叉验证import numpy as np import pandas as pd im

我正在尝试使用从http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/改编的以下代码在XGBC分类器上执行多类分类问题的交叉验证

import numpy as npimport pandas as pdimport xgboost as xgbfrom xgboost.sklearn import  XGBClassifIErfrom sklearn.preprocessing import LabelEncoderfrom sklearn import cross_valIDation,metricsfrom sklearn.grID_search import gridsearchcvdef modelFit(alg,X,y,useTrainCV=True,cvFolds=5,early_stopPing_rounds=50):    if useTrainCV:        xgbParams = alg.get_xgb_params()        xgTrain = xgb.DMatrix(X,label=y)        cvresult = xgb.cv(xgbParams,xgTrain,num_boost_round=alg.get_params()['n_estimators'],nfold=cvFolds,stratifIEd=True,metrics={'mlogloss'},early_stopPing_rounds=early_stopPing_rounds,seed=0,callbacks=[xgb.callback.print_evaluation(show_stdv=False),xgb.callback.early_stop(3)])        print cvresult        alg.set_params(n_estimators=cvresult.shape[0])    # Fit the algorithm    alg.fit(X,eval_metric='mlogloss')    # Predict    dtrainPredictions = alg.predict(X)    dtrainPredProb = alg.predict_proba(X)    # Print model report:    print "\nModel Report"    print "Classification report: \n"    print(classification_report(y_val,y_val_pred))    print "Accuracy : %.4g" % metrics.accuracy_score(y,dtrainPredictions)    print "Log Loss score (Train): %f" % metrics.log_loss(y,dtrainPredProb)    feat_imp = pd.SerIEs(alg.booster().get_fscore()).sort_values(ascending=False)    feat_imp.plot(kind='bar',Title='Feature importances')    plt.ylabel('Feature importance score')# 1) Read training setprint('>> Read training set')train = pd.read_csv(trainfile)# 2) Extract target attribute and convert to numericprint('>> Preprocessing')y_train = train['OutcomeType'].valuesle_y = LabelEncoder()y_train = le_y.fit_transform(y_train)train.drop('OutcomeType',axis=1,inplace=True)# 4) Extract features and target from training setX_train = train.values# 5) First classifIErxgb = XGBClassifIEr(learning_rate =0.1,n_estimators=1000,max_depth=5,min_child_weight=1,gamma=0,subsample=0.8,colsample_bytree=0.8,scale_pos_weight=1,objective='multi:softprob',seed=27)modelFit(xgb,X_train,y_train)

其中y_train包含从0到4的标签.但是,当我运行此代码时,我从xgb.cv函数xgboost.core.XGBoostError得到以下错误：参数num_class的值0应该大于等于1.在XGBoost doc上我读了在多类情况下,xgb从目标向量中的标签中推断出类的数量,所以我不明白发生了什么.最佳答案您必须将参数’num_class’添加到xgb_param字典中.参数说明和上面提供的链接中的注释中也提到了这一点. 总结

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