Threshold tuning in ML Python

Introduction

Threshold tuning helps decide the best cutoff point to say if a prediction is positive or negative. It improves how well a model makes decisions.

When you want to balance between catching all positive cases and avoiding false alarms.

When the cost of wrong decisions is different for positive and negative results.

When your model gives probabilities but you need clear yes/no answers.

When you want to improve metrics like precision, recall, or F1 score.

When you want to customize the model behavior for specific business needs.

Syntax

ML Python

for threshold in thresholds:
    predictions = (probabilities >= threshold).astype(int)
    metric_value = metric(true_labels, predictions)

Thresholds are values between 0 and 1.

Probabilities come from model outputs like logistic regression or neural networks.

Examples

Use 0.5 as the cutoff to decide positive or negative.

ML Python

threshold = 0.5
predictions = (probabilities >= threshold).astype(int)

Try multiple thresholds to find the best one.

ML Python

thresholds = [0.3, 0.5, 0.7]
for t in thresholds:
    preds = (probabilities >= t).astype(int)

Select the threshold that gives the highest metric score.

ML Python

best_threshold = thresholds[np.argmax(metric_scores)]

Sample Model

This code tests thresholds from 0.0 to 1.0 in steps of 0.1. It calculates the F1 score for each threshold and finds the best one.

ML Python

import numpy as np
from sklearn.metrics import f1_score

# True labels
true_labels = np.array([0, 1, 0, 1, 1, 0, 1, 0])

# Model predicted probabilities
probabilities = np.array([0.1, 0.4, 0.35, 0.8, 0.7, 0.2, 0.9, 0.05])

# Define thresholds to test
thresholds = np.arange(0.0, 1.01, 0.1)

best_threshold = 0.0
best_f1 = 0.0

for threshold in thresholds:
    predictions = (probabilities >= threshold).astype(int)
    score = f1_score(true_labels, predictions)
    print(f"Threshold: {threshold:.1f}, F1 Score: {score:.2f}")
    if score > best_f1:
        best_f1 = score
        best_threshold = threshold

print(f"\nBest threshold: {best_threshold:.1f} with F1 Score: {best_f1:.2f}")

Important Notes

Lower thresholds catch more positives but may increase false alarms.

Higher thresholds reduce false alarms but may miss positives.

Choose threshold based on what matters more: catching positives or avoiding false alarms.

Summary

Threshold tuning helps pick the best cutoff for yes/no decisions from probabilities.

Try different thresholds and check metrics like F1 score to find the best one.

Adjust threshold to balance between catching positives and avoiding false alarms.

Practice

1. What is the main purpose of threshold tuning in machine learning classification?

easy

A. To find the best cutoff probability to decide between classes

B. To increase the size of the training dataset

C. To reduce the number of features used in the model

D. To speed up the training process

Solution

1. Step 1: Understand threshold tuning concept

   Threshold tuning is about choosing a cutoff value for predicted probabilities to decide class labels.

2. Step 2: Identify the main goal

   The goal is to find the cutoff that best separates positive and negative classes for better decisions.

3. Final Answer:

   To find the best cutoff probability to decide between classes -> Option A

4. Quick Check:

   Threshold tuning = best cutoff choice [OK]

Hint: Threshold tuning picks the cutoff to decide yes/no [OK]

Common Mistakes:

• Confusing threshold tuning with feature selection
• Thinking threshold tuning changes training data size
• Assuming threshold tuning speeds up training

2. Which of the following is the correct way to apply a threshold of 0.7 to predicted probabilities probs in Python to get binary predictions?

easy

A. preds = (probs > 0.7).astype(int)

B. preds = probs > 0.7

C. preds = int(probs > 0.7)

D. preds = probs >= 0.7

Solution

1. Step 1: Understand threshold application

   We compare each probability to 0.7 to get True/False, then convert to 0/1 integers.

2. Step 2: Check correct syntax

   Using (probs > 0.7).astype(int) converts boolean array to integer array correctly.

3. Final Answer:

   preds = (probs > 0.7).astype(int) -> Option A

4. Quick Check:

   Threshold applied with boolean then int cast [OK]

Hint: Use boolean comparison then convert to int for binary labels [OK]

Common Mistakes:

• Forgetting to convert boolean to int
• Using int() on entire array instead of element-wise
• Using >= instead of > changes threshold logic

3. Given the following code, what will be the printed F1 score after threshold tuning?

from sklearn.metrics import f1_score
probs = [0.2, 0.8, 0.6, 0.4]
true_labels = [0, 1, 1, 0]
threshold = 0.5
preds = [1 if p > threshold else 0 for p in probs]
f1 = f1_score(true_labels, preds)
print(round(f1, 2))

medium

A. 0.80

B. 0.67

C. 1.00

D. 0.50

Solution

1. Step 1: Calculate predictions with threshold 0.5

   probs > 0.5 gives preds = [0, 1, 1, 0]

2. Step 2: Compute F1 score for preds vs true_labels

   True positives = 2, false positives = 0, false negatives = 0, so F1 = 2*TP/(2*TP+FP+FN) = 2*2/(4+0+0) = 1.0, since preds and true_labels are identical.

3. Final Answer:

   1.00 -> Option C

4. Quick Check:

   Perfect match means F1 = 1.00 [OK]

Hint: Check predicted labels carefully before scoring [OK]

Common Mistakes:

• Miscomputing predictions from threshold
• Confusing precision and recall in F1 calculation
• Rounding errors in final score

4. The following code tries to tune threshold but gives an error. What is the error?

probs = [0.1, 0.4, 0.6, 0.9]
true_labels = [0, 0, 1, 1]
thresholds = [0.3, 0.5, 0.7]
best_f1 = 0
for t in thresholds:
    preds = (probs > t)
    f1 = f1_score(true_labels, preds)
    if f1 > best_f1:
        best_f1 = f1
print(best_f1)

medium

A. Thresholds list is empty

B. Missing import of f1_score

C. preds is boolean, should be integers

D. Loop variable t is not used

Solution

1. Step 1: Check code for missing imports

   The code uses f1_score but does not import it from sklearn.metrics.

2. Step 2: Identify error cause

   Without importing f1_score, Python will raise a NameError when calling f1_score.

3. Final Answer:

   Missing import of f1_score -> Option B

4. Quick Check:

   Always import functions before use [OK]

Hint: Check if all functions are imported before use [OK]

Common Mistakes:

• Assuming boolean preds cause error (they don't)
• Ignoring missing import errors
• Thinking loop variable is unused

5. You have a model predicting probabilities for a rare disease. You want to tune the threshold to catch as many sick patients as possible but avoid too many false alarms. Which approach best balances this trade-off?

hard

A. Choose threshold maximizing recall only

B. Choose threshold minimizing accuracy

C. Choose threshold maximizing precision only

D. Choose threshold maximizing F1 score

Solution

1. Step 1: Understand the trade-off

   High recall catches more sick patients but may increase false alarms; precision reduces false alarms but may miss sick patients.

2. Step 2: Identify best metric for balance

   F1 score balances precision and recall, making it best to tune threshold for this trade-off.

3. Final Answer:

   Choose threshold maximizing F1 score -> Option D

4. Quick Check:

   F1 balances recall and precision [OK]

Hint: Use F1 score to balance recall and precision [OK]

Common Mistakes:

• Maximizing recall ignores false alarms
• Maximizing precision ignores missed cases
• Minimizing accuracy is not meaningful