Complete the code to set the threshold for binary classification.
threshold = [1]The typical threshold for binary classification is 0.5, meaning predictions above 0.5 are positive.
Complete the code to convert probabilities to binary predictions using the threshold.
predictions = (probabilities > [1]).astype(int)Using 0.5 as threshold converts probabilities above 0.5 to class 1, else 0.
Fix the error in the code to compute true positives using the threshold.
true_positives = ((y_true == 1) & (y_scores > [1])).sum()
The threshold 0.5 is used to decide positive predictions from scores.
Fill both blanks to create a dictionary of thresholds and their corresponding F1 scores.
f1_scores = {thr: f1_score(y_true, (y_scores > [1]).astype(int)) for thr in [2]Use 'thr' as the threshold variable and a list of threshold values to compute F1 scores.
Fill all three blanks to select the best threshold based on maximum F1 score.
best_threshold = max(f1_scores, key=lambda [1]: f1_scores[[2]]) print(f"Best threshold: [3]")
Use 'thr' as the lambda argument and key to find max, then print best_threshold.