'how to set threshold to scikit learn random forest model

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 1:^[1]

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, y_train)

threshold = 0.4

predicted = random_forest.predict_proba(X_test)
predicted[:,0] = (predicted[:,0] < threshold).astype('int')
predicted[:,1] = (predicted[:,1] >= threshold).astype('int')


accuracy = accuracy_score(y_test, predicted)
print(round(accuracy,4,)*100, "%")

this comes with an error refers to the last accuracy part" ValueError: Can't handle mix of binary and multilabel-indicator"

Solution 2:^[2]

sklearn.metrics.accuracy_score takes 1 d array but your predicted array is 2-d. This comes with an error.
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html

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Solution 2

'how to set threshold to scikit learn random forest model

Solution 1:[1]

Solution 2:[2]

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Solution 1:^[1]

Solution 2:^[2]