[FIXED] how to set threshold to scikit learn random forest model

Issue

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)

Documentation Precision recall

Solution

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)

Answered By - Stev