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我recursive feature elimination with cross validation (rfecv)用作以下功能选择器randomforest classifier。

X = df[[my_features]] #all my features
y = df['gold_standard'] #labels

clf = RandomForestClassifier(random_state = 42, class_weight="balanced")
rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(10), scoring='roc_auc')
rfecv.fit(X,y)

print("Optimal number of features : %d" % rfecv.n_features_)
features=list(X.columns[rfecv.support_])

我还执行GridSearchCV以下操作，以调整以下超参数RandomForestClassifier。

X = df[[my_features]] #all my features
y = df['gold_standard'] #labels

x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0)

rfc = RandomForestClassifier(random_state=42, class_weight = 'balanced')
param_grid = { 
    'n_estimators': [200, 500],
    'max_features': …