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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
818
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.
Yes, you can easily do this. A sklearn Decision Tree exposes its underlying tree through the tree_ attribute. This tree_ , among other things, have an attribute threshold , which is a numpy array containing threshold values of all nodes. You can modify this array, thereby changing the thresholds.
In binary classification, when a model gives us a score instead of the prediction itself, we usually need to convert this score into a prediction applying a threshold. Since the meaning of the score is to give us the perceived probability of having 1 according to our model, it's obvious to use 0.5 as a threshold.
ROC curve for finding the optimal thresholdThe X-axis or independent variable is the false positive rate for the predictive test. The Y-axis or dependent variable is the true positive rate for the predictive test. A perfect result would be the point (0, 1) indicating 0% false positives and 100% true positives.
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)
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"
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