Threshold tuning in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the effect of threshold on classification

In a binary classification model, what happens to the precision and recall when you increase the decision threshold from 0.5 to 0.8?

A Both precision and recall increase

B Precision decreases and recall increases

C Precision increases and recall decreases

D Both precision and recall decrease

💡 Hint

Think about how raising the threshold affects which predictions are labeled positive.

Increasing the threshold means the model is more strict to label positives, so fewer false positives occur, increasing precision. But some true positives are missed, lowering recall.

❓ Predict Output

intermediate

Output of threshold tuning code snippet

What is the output of the following Python code that applies threshold tuning on model probabilities?

ML Python

import numpy as np
probs = np.array([0.2, 0.6, 0.8, 0.4, 0.9])
threshold = 0.7
preds = (probs >= threshold).astype(int)
print(preds.tolist())

A [0, 1, 0, 0, 1]

B [0, 0, 1, 0, 1]

C [0, 1, 1, 0, 1]

D [1, 1, 1, 0, 1]

💡 Hint

Check which probabilities are greater than or equal to 0.7.

Only 0.8 and 0.9 are >= 0.7, so their predictions are 1; others are 0.

❓ Model Choice

advanced

Choosing threshold for imbalanced data

You have a highly imbalanced dataset with very few positive cases. Which threshold tuning strategy is best to maximize recall while keeping false positives reasonable?

A Randomly select threshold to avoid bias

B Set a high threshold to reduce false positives, ignoring recall

C Use threshold 0.5 without tuning since it is default

D Set a low threshold to catch most positives, then use precision-recall curve to find balance

💡 Hint

Recall is about catching positives; think about how threshold affects it.

Lowering threshold increases recall but may increase false positives. Using precision-recall curve helps find a good trade-off.

❓ Metrics

advanced

Effect of threshold on F1 score

Given a model with these confusion matrix values at threshold 0.5: TP=40, FP=10, FN=20, TN=130. If threshold is increased to 0.7, TP=30, FP=5, FN=30, TN=135. What happens to the F1 score?

A F1 score decreases

B F1 score increases

C F1 score stays the same

D Cannot determine without precision and recall

💡 Hint

Calculate precision and recall for both thresholds, then compute F1.

At 0.5 threshold: precision=40/(40+10)=0.8, recall=40/(40+20)=0.67, F1≈0.73. At 0.7 threshold: precision=30/(30+5)=0.86, recall=30/(30+30)=0.5, F1≈0.63. F1 decreases.

🔧 Debug

expert

Debugging threshold tuning code with unexpected output

Consider this Python code snippet for threshold tuning. It produces unexpected predictions. What is the cause?

import numpy as np
probs = np.array([0.3, 0.7, 0.5, 0.9])
threshold = 0.5
preds = (probs > threshold).astype(int)
print(preds.tolist())

A Using '>' excludes probabilities equal to threshold, causing some positives to be missed

B The threshold variable is not used correctly; should be a list

C astype(int) converts floats incorrectly causing wrong predictions

D Numpy array probs is not sorted, causing wrong output

💡 Hint

Check how the comparison operator affects predictions equal to threshold.

The code uses '>' which excludes probabilities exactly equal to 0.5, so those are predicted negative. Using '>=' includes them.

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