Classification evaluation (accuracy, precision, recall, F1) in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is accuracy in classification evaluation?

Accuracy is the ratio of correct predictions to the total number of predictions made. It tells us how often the model is right overall.

beginner

Define precision in classification tasks.

Precision measures how many of the items predicted as positive are actually positive. It shows the model's exactness or quality of positive predictions.

beginner

What does recall tell us in classification?

Recall tells us how many of the actual positive cases the model correctly found. It measures the model's ability to find all positive examples.

intermediate

Explain the F1 score and why it is useful.

The F1 score is the harmonic mean of precision and recall. It balances both, giving a single score that considers both false positives and false negatives.

intermediate

Why might accuracy be misleading in some classification problems?

Accuracy can be misleading when classes are imbalanced. For example, if 95% of data is one class, a model predicting only that class gets 95% accuracy but fails to detect the other class.

Which metric tells you the proportion of true positives out of all predicted positives?

A Precision

B Recall

C Accuracy

D F1 score

Precision measures how many predicted positives are actually correct.

If a model has high recall but low precision, what does it mean?

A It balances false positives and false negatives well

B It misses many positives but has few false positives

C It finds most positives but also has many false positives

D It is very accurate overall

High recall means most positives are found, low precision means many false positives.

What is the F1 score a balance of?

A Precision and recall

B Accuracy and recall

C Accuracy and precision

D True positives and true negatives

F1 score balances precision and recall into one metric.

Why is accuracy not always the best metric for classification?

A Because it ignores false negatives

B Because it ignores false positives

C Because it only works for binary classification

D Because it can be high even if the model misses rare classes

Accuracy can be misleading when classes are imbalanced.

Recall is also known as:

A False positive rate

B True positive rate

C Precision

D Accuracy

Recall measures the true positive rate.

Describe in your own words what precision and recall measure in classification.

Think about how many predicted positives are right vs. how many actual positives are found.

Explain why the F1 score is useful when evaluating a classification model.

Consider situations where precision and recall differ a lot.

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