Classification evaluation (accuracy, precision, recall, F1) in ML Python

Introduction

We use classification evaluation to check how well a model guesses categories. It helps us know if the model is good or needs improvement.

When you want to see how many emails your spam filter correctly marks as spam or not spam.

When checking if a medical test correctly identifies sick and healthy patients.

When evaluating if a model correctly classifies images of cats and dogs.

When comparing different models to pick the best one for sorting customer reviews as positive or negative.

Syntax

ML Python

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

accuracy = accuracy_score(true_labels, predicted_labels)
precision = precision_score(true_labels, predicted_labels)
recall = recall_score(true_labels, predicted_labels)
f1 = f1_score(true_labels, predicted_labels)

accuracy_score measures overall correct guesses.

precision_score measures how many predicted positives are actually positive.

recall_score measures how many actual positives were found.

f1_score balances precision and recall into one number.

Examples

Calculates accuracy for 4 predictions.

ML Python

accuracy = accuracy_score([1,0,1,1], [1,0,0,1])

Calculates precision for binary labels.

ML Python

precision = precision_score([1,0,1,1], [1,0,0,1])

Calculates recall for binary labels.

ML Python

recall = recall_score([1,0,1,1], [1,0,0,1])

Calculates F1 score for binary labels.

ML Python

f1 = f1_score([1,0,1,1], [1,0,0,1])

Sample Model

This program compares true labels and predicted labels, then prints four common evaluation scores to see how well the model did.

ML Python

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# True labels (actual categories)
true_labels = [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]

# Predicted labels by the model
predicted_labels = [1, 0, 1, 0, 0, 1, 1, 0, 1, 0]

# Calculate evaluation metrics
accuracy = accuracy_score(true_labels, predicted_labels)
precision = precision_score(true_labels, predicted_labels)
recall = recall_score(true_labels, predicted_labels)
f1 = f1_score(true_labels, predicted_labels)

print(f"Accuracy: {accuracy:.2f}")
print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"F1 Score: {f1:.2f}")

Important Notes

Accuracy can be misleading if classes are imbalanced (one class is much bigger).

Precision is important when false positives are costly (e.g., wrongly flagging emails as spam).

Recall is important when missing positives is costly (e.g., missing sick patients).

Summary

Accuracy shows overall correct predictions.

Precision and recall focus on positive class quality.

F1 score balances precision and recall into one number.

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