Python xgb.cv'；当'colsample_bytree'不是1时，s auc分数与cross_val_分数不匹配
我正在处理高度不平衡的数据集。在超参数调优过程中，我发现如果Python xgb.cv'；当'colsample_bytree'不是1时，s auc分数与cross_val_分数不匹配,python,cross-validation,xgboost,k-fold,xgbclassifier,Python,Cross Validation,Xgboost,K Fold,Xgbclassifier,我正在处理高度不平衡的数据集。在超参数调优过程中，我发现如果colssample_bytree设置为除1以外的值，则sklearn软件包中的cross_val_得分与从xgb.cv获得的auc得分不匹配 xgb.cv代码： # creating kfolds kfolds = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 16) # creating model object and using it for xgb.
colssample_bytreecross_val_得分xgb.cv# creating kfolds
kfolds = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 16)
# creating model object and using it for xgb.cv
xgb0 = XGBClassifier(objective= 'binary:logistic', n_estimators =2, colsample_bytree = 0.6,
random_state =16, n_jobs = -1, eval_metric = 'auc')
params = xgb0.get_params()
xg_train = xgb.DMatrix(X_train_p.values, label = y_train.values)
cv_result = xgb.cv(params, xg_train, num_boost_round=2, folds = kfolds, metrics = 'auc', early_stopping_rounds = 50,
as_pandas = True, seed = 16,stratified=True, shuffle = True)
print(cv_result['test-auc-mean'].values[-1])
cv_score = cross_val_score(xgb0, X_train_p, y_train, cv = kfolds, n_jobs = -1, scoring = 'roc_auc')
cv_score.mean()
# creating kfolds
kfolds = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 16)
# creating model object and using it for xgb.cv
xgb0 = XGBClassifier(objective= 'binary:logistic', n_estimators =2, colsample_bytree = 0.6,
random_state =16, n_jobs = -1, eval_metric = 'auc')
params = xgb0.get_params()
xg_train = xgb.DMatrix(X_train_p.values, label = y_train.values)
cv_result = xgb.cv(params, xg_train, num_boost_round=2, folds = kfolds, metrics = 'auc', early_stopping_rounds = 50,
as_pandas = True, seed = 16,stratified=True, shuffle = True)
print(cv_result['test-auc-mean'].values[-1])
cv_score = cross_val_score(xgb0, X_train_p, y_train, cv = kfolds, n_jobs = -1, scoring = 'roc_auc')
cv_score.mean()
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