如何为 scikit 学习随机森林模型设置阈值

问题描述

看到precision_recall_curve后，如果我想设置threshold = 0.4，如何将0.4实现到我的随机森林模型中(二分类)，对于任何概率=0.4，标记将其设为 1.

After seeing the precision_recall_curve, if I want to set threshold = 0.4, how to implement 0.4 into my random forest model (binary classification), for any probability <0.4, label it as 0, for any >=0.4, label it as 1.

from sklearn.ensemble import RandomForestClassifier
  random_forest = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=12)
  random_forest.fit(X_train, y_train)
from sklearn.metrics import accuracy_score
  predicted = random_forest.predict(X_test)
accuracy = accuracy_score(y_test, predicted)

文档精确召回

推荐答案

假设你在做二元分类，这很容易:

Assuming you are doing binary classification, it's quite easy:

threshold = 0.4

predicted_proba = random_forest.predict_proba(X_test)
predicted = (predicted_proba [:,1] >= threshold).astype('int')

accuracy = accuracy_score(y_test, predicted)

这篇关于如何为 scikit 学习随机森林模型设置阈值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！