如何在sklearn中的randomforest中获得决策函数 [英] How to get decision function in randomforest in sklearn

问题描述

我正在使用以下代码，通过 gridsearchcv 获取 randomforest 的优化参数.

I am using the following code to get the optimised parameters for randomforest using gridsearchcv.

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': ['auto', 'sqrt', 'log2'],
    'max_depth' : [4,5,6,7,8],
    'criterion' :['gini', 'entropy']
}
k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 10, scoring = 'roc_auc')
CV_rfc.fit(x_train, y_train)
print(CV_rfc.best_params_)
print(CV_rfc.best_score_)

现在，我想将调整后的参数应用于 X_test .为此，我做了以下事情，

Now, I want to apply the tuned parameters to X_test. For that I did the following,

pred = CV_rfc.decision_function(x_test)
print(roc_auc_score(y_test, pred))

但是，由于出现以下错误， decision_function 似乎不支持 randomforest .

However, decision_function does not seem to support randomforest as I got the following error.

AttributeError:"RandomForestClassifier"对象没有属性'decision_function'.

AttributeError: 'RandomForestClassifier' object has no attribute 'decision_function'.

还有其他方法吗?

如果需要，我很乐意提供更多详细信息.

I am happy to provide more details if needed.

推荐答案

如果您打算获得模型评分功能，以便可以对 auc_roc_score 使用评分，则可以进行 predict_proba()

If your intention is to get a model scoring function so that the scoring can be used for auc_roc_score, then you can go for predict_proba()

y_pred_proba = CV_rfc.predict_proba(x_test)
print(roc_auc_score(y_test, y_pred_proba[:,1]))

这篇关于如何在sklearn中的randomforest中获得决策函数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！