Threshold tuning in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the threshold for binary classification.

ML Python

threshold = [1]

Drag options to blanks, or click blank then click option'

A0

B1.0

C0.5

D-0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using threshold values outside the 0-1 range.

Setting threshold to 0 or 1 which makes classification trivial.

The typical threshold for binary classification is 0.5, meaning predictions above 0.5 are positive.

2fill in blank

medium

Complete the code to convert probabilities to binary predictions using the threshold.

ML Python

predictions = (probabilities > [1]).astype(int)

Drag options to blanks, or click blank then click option'

A1.0

B0.5

C0.7

D0.3

Attempts:

3 left

💡 Hint

Common Mistakes

Using threshold greater than 1 or less than 0.

Not converting boolean array to integers.

Using 0.5 as threshold converts probabilities above 0.5 to class 1, else 0.

3fill in blank

hard

Fix the error in the code to compute true positives using the threshold.

ML Python

true_positives = ((y_true == 1) & (y_scores > [1])).sum()

Drag options to blanks, or click blank then click option'

A0

B-1

C1

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using threshold 0 or 1 which may not capture positives correctly.

Using negative threshold values.

The threshold 0.5 is used to decide positive predictions from scores.

4fill in blank

hard

Fill both blanks to create a dictionary of thresholds and their corresponding F1 scores.

ML Python

f1_scores = {thr: f1_score(y_true, (y_scores > [1]).astype(int)) for thr in [2]

Drag options to blanks, or click blank then click option'

Athr

B[0.1, 0.3, 0.5, 0.7, 0.9]

Cthresholds

Dy_scores

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names for threshold or list.

Not converting boolean to int before computing F1 score.

Use 'thr' as the threshold variable and a list of threshold values to compute F1 scores.

5fill in blank

hard

Fill all three blanks to select the best threshold based on maximum F1 score.

ML Python

best_threshold = max(f1_scores, key=lambda [1]: f1_scores[[2]])
print(f"Best threshold: [3]")

Drag options to blanks, or click blank then click option'

Athr

Cbest_threshold

Dthreshold

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inside lambda and key lookup.

Printing a variable that is not defined.

Use 'thr' as the lambda argument and key to find max, then print best_threshold.

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