Classification evaluation (accuracy, precision, recall, F1) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate accuracy score using sklearn.

ML Python

from sklearn.metrics import [1]

accuracy = [1](y_true, y_pred)
print(f"Accuracy: {accuracy:.2f}")

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

Aaccuracy_score

Bprecision_score

Crecall_score

Df1_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using precision_score or recall_score instead of accuracy_score.

The accuracy_score function calculates the ratio of correct predictions to total predictions.

2fill in blank

medium

Complete the code to calculate precision score for binary classification.

ML Python

from sklearn.metrics import precision_score

precision = precision_score(y_true, y_pred, [1]=1)
print(f"Precision: {precision:.2f}")

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

Azero_division

Baverage

Clabels

Dpos_label

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'average' parameter incorrectly for binary precision.

The pos_label parameter specifies which class is considered positive for precision calculation.

3fill in blank

hard

Fix the error in the code to calculate recall score correctly.

ML Python

from sklearn.metrics import recall_score

recall = recall_score(y_true, y_pred, average=[1])
print(f"Recall: {recall:.2f}")

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

A'binary'

B'micro'

C'macro'

D'weighted'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'micro' or 'macro' average for binary recall.

For binary classification, the average parameter should be set to 'binary' to calculate recall correctly.

4fill in blank

hard

Fill both blanks to calculate F1 score with macro averaging.

ML Python

from sklearn.metrics import f1_score

f1 = f1_score(y_true, y_pred, [1]=[2])
print(f"F1 Score: {f1:.2f}")

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

Aaverage

B'macro'

C'binary'

D'weighted'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'binary' average for multi-class F1 score.

The average parameter set to 'macro' calculates the F1 score by averaging scores for each class equally.

5fill in blank

hard

Fill all three blanks to create a dictionary of classification metrics.

ML Python

metrics = {
    'accuracy': [1](y_true, y_pred),
    'precision': [2](y_true, y_pred, average='macro'),
    'recall': [3](y_true, y_pred, average='macro')
}
print(metrics)

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

Aaccuracy_score

Bprecision_score

Crecall_score

Df1_score

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up function names or using f1_score instead of recall_score.

This dictionary stores accuracy, precision, and recall scores using the correct sklearn functions.

Practice

1. Which metric tells you the overall percentage of correct predictions in a classification model?

easy

A. Recall

B. Precision

C. F1 Score

D. Accuracy

Solution

1. Step 1: Understand what accuracy measures

   Accuracy is the ratio of correct predictions (both true positives and true negatives) to total predictions.

2. Step 2: Compare with other metrics

   Precision and recall focus only on positive class predictions, while F1 score balances precision and recall. Only accuracy measures overall correctness.

3. Final Answer:

   Accuracy -> Option D

4. Quick Check:

   Overall correct predictions = Accuracy [OK]

Hint: Accuracy = correct predictions / total predictions [OK]

Common Mistakes:

• Confusing precision with accuracy
• Thinking recall measures overall correctness
• Mixing F1 score with accuracy

2. Which of the following formulas correctly calculates precision?

easy

A. True Positives / (True Positives + False Positives)

B. 2 * (Precision * Recall) / (Precision + Recall)

C. (True Positives + True Negatives) / Total Predictions

D. True Positives / (True Positives + False Negatives)

Solution

1. Step 1: Recall the definition of precision

   Precision measures how many predicted positives are actually correct, so it is True Positives divided by all predicted positives.

2. Step 2: Match formula to definition

   True Positives + False Positives equals all predicted positives, so precision = TP / (TP + FP).

3. Final Answer:

   True Positives / (True Positives + False Positives) -> Option A

4. Quick Check:

   Precision = TP / (TP + FP) [OK]

Hint: Precision = correct positive predictions / all positive predictions [OK]

Common Mistakes:

• Using false negatives instead of false positives
• Confusing precision with recall formula
• Choosing accuracy formula by mistake

3. Given the confusion matrix:
TP=40, FP=10, FN=5, TN=45
What is the F1 score?

medium

A. 0.80

B. 0.88

C. 0.84

D. 0.90

Solution

1. Step 1: Calculate precision and recall

   Precision = TP / (TP + FP) = 40 / (40 + 10) = 40 / 50 = 0.8
   Recall = TP / (TP + FN) = 40 / (40 + 5) = 40 / 45 ≈ 0.8889

2. Step 2: Calculate F1 score using formula

   F1 = 2 * (Precision * Recall) / (Precision + Recall) = 2 * (0.8 * 0.8889) / (0.8 + 0.8889) ≈ 2 * 0.7111 / 1.6889 ≈ 1.4222 / 1.6889 ≈ 0.841

3. Final Answer:

   0.84 -> Option C

4. Quick Check:

   F1 balances precision and recall = 0.84 [OK]

Hint: F1 = 2 * (P * R) / (P + R), calculate P and R first [OK]

Common Mistakes:

• Mixing up false positives and false negatives
• Using accuracy instead of F1
• Rounding too early causing wrong final answer

4. You have this Python code to calculate recall:

def recall(tp, fn):
    return tp / (tp + fn + 1)

What is the error in this code?

medium

A. Adding 1 to denominator incorrectly lowers recall

B. Missing parentheses around tp + fn

C. Recall should use false positives, not false negatives

D. Function should return precision, not recall

Solution

1. Step 1: Understand recall formula

   Recall = TP / (TP + FN). The code adds +1 to denominator, which is not part of the formula.

2. Step 2: Identify effect of +1 in denominator

   Adding 1 artificially increases denominator, lowering recall value incorrectly.

3. Final Answer:

   Adding 1 to denominator incorrectly lowers recall -> Option A

4. Quick Check:

   Recall denominator = TP + FN only [OK]

Hint: Recall denominator must be TP + FN exactly, no extras [OK]

Common Mistakes:

• Thinking +1 is needed to avoid division by zero
• Confusing recall with precision formula
• Ignoring parentheses impact

5. A model has precision 0.75 and recall 0.60. You want to improve the F1 score. Which action is best?

hard

A. Increase precision by reducing false negatives

B. Increase recall by reducing false negatives

C. Increase accuracy by increasing true negatives

D. Increase recall by increasing false positives

Solution

1. Step 1: Understand F1 score depends on precision and recall

   F1 score balances precision and recall. Improving either precision or recall can increase F1.

2. Step 2: Identify how to improve recall

   Recall = TP / (TP + FN). Reducing false negatives (FN) increases recall.

3. Step 3: Evaluate options

   Increase recall by reducing false negatives increases recall by reducing FN, improving F1.
   Increase precision by reducing false negatives incorrectly links precision with false negatives.
   Increase accuracy by increasing true negatives affects accuracy, not F1 directly.
   Increase recall by increasing false positives, but increasing false positives lowers precision, hurting F1.

4. Final Answer:

   Increase recall by reducing false negatives -> Option B

5. Quick Check:

   Reduce FN to boost recall and F1 [OK]

Hint: Lower false negatives to raise recall and F1 score [OK]

Common Mistakes:

• Confusing false positives with false negatives
• Thinking accuracy always improves F1
• Increasing false positives to boost recall