我使用交叉验证(rfecv)的递归特征消除作为GridSearchCV的特征选择技术.
我的代码如下.
X = df[my_features_all]
y = df['gold_standard']
x_train,x_test,y_train,y_test = train_test_split(X,y,random_state=0)
k_fold = StratifiedKFold(n_splits=5,shuffle=True,random_state=0)
clf = RandomForestClassifier(random_state = 42,class_weight="balanced")
rfecv = RFECV(estimator=clf,step=1,cv=k_fold,scoring='roc_auc')
param_grid = {'estimator__n_estimators': [200,500],'estimator__max_features': ['auto','sqrt','log2'],'estimator__max_depth' : [3,4,5]
}
CV_rfc = GridSearchCV(estimator=rfecv,param_grid=param_grid,cv= k_fold,scoring = 'roc_auc',verbose=10,n_jobs = 5)
CV_rfc.fit(x_train,y_train)
print("Finished feature selection and parameter tuning")
为此,我运行了以下代码.
#feature selection results
print("Optimal number of features : %d" % rfecv.n_features_)
features=list(X.columns[rfecv.support_])
print(features)
但是,出现以下错误：
AttributeError：“ RFECV”对象没有属性“ n_features_”.
如果需要,我很乐意提供更多详细信息.
最佳答案
您传递给GridSearchCV的对象rfecv不适合它.首先将其克隆,然后将这些克隆拟合至数据并评估超参数的所有不同组合.
原文链接：/python/533307.html因此,要访问最佳功能,您需要访问GridSearchCV的best_estimator_属性：
CV_rfc.fit(x_train,y_train)
print("Finished feature selection and parameter tuning")
print("Optimal number of features : %d" % rfecv.n_features_)
features=list(X.columns[CV_rfc.best_estimator_.support_])
print(features)