Evaluation metrics (accuracy, F1, confusion matrix) in NLP

Introduction

We use evaluation metrics to see how well a model is doing. They help us understand if the model makes good predictions or not.

Checking if a spam filter correctly identifies spam emails.

Measuring how well a model classifies positive and negative movie reviews.

Evaluating a model that detects diseases from medical reports.

Comparing different models to pick the best one for a task.

Understanding mistakes a model makes to improve it.

Syntax

NLP

from sklearn.metrics import accuracy_score, f1_score, confusion_matrix

# accuracy = accuracy_score(true_labels, predicted_labels)
# f1 = f1_score(true_labels, predicted_labels, average='binary')
# cm = confusion_matrix(true_labels, predicted_labels)

accuracy_score measures the percentage of correct predictions.

f1_score balances precision and recall, useful when classes are uneven.

Examples

Calculate accuracy, F1 score, and confusion matrix for a small example.

NLP

accuracy = accuracy_score([1,0,1,1], [1,0,0,1])
f1 = f1_score([1,0,1,1], [1,0,0,1])
cm = confusion_matrix([1,0,1,1], [1,0,0,1])

Calculate F1 score for multi-class data using macro average.

NLP

f1_macro = f1_score([0,1,2,2], [0,2,1,2], average='macro')

Sample Model

This program shows how to calculate accuracy, F1 score, and confusion matrix for a simple binary classification example.

NLP

from sklearn.metrics import accuracy_score, f1_score, confusion_matrix

# True labels for 8 samples
true_labels = [0, 1, 0, 1, 0, 1, 1, 0]
# Model predictions
predicted_labels = [0, 0, 0, 1, 0, 1, 0, 1]

# Calculate accuracy
accuracy = accuracy_score(true_labels, predicted_labels)
# Calculate F1 score (binary)
f1 = f1_score(true_labels, predicted_labels)
# Calculate confusion matrix
cm = confusion_matrix(true_labels, predicted_labels)

print(f"Accuracy: {accuracy:.2f}")
print(f"F1 Score: {f1:.2f}")
print("Confusion Matrix:")
print(cm)

Important Notes

Accuracy can be misleading if classes are imbalanced.

F1 score is better when you care about both false positives and false negatives.

The confusion matrix shows counts of true negatives, false positives, false negatives, and true positives.

Summary

Accuracy tells how many predictions were correct overall.

F1 score balances precision and recall, useful for uneven classes.

Confusion matrix helps see where the model makes mistakes.

Practice

1. What does the accuracy metric measure in a classification model?

easy

A. The proportion of correct predictions out of all predictions

B. The balance between precision and recall

C. The number of false positives only

D. The total number of classes in the dataset

Solution

1. Step 1: Understand accuracy definition

   Accuracy is defined as the number of correct predictions divided by the total number of predictions made.

2. Step 2: Compare options with definition

   Only The proportion of correct predictions out of all predictions correctly describes accuracy as the proportion of correct predictions out of all predictions.

3. Final Answer:

   The proportion of correct predictions out of all predictions -> Option A

4. Quick Check:

   Accuracy = Correct predictions / Total predictions [OK]

Hint: Accuracy = correct predictions divided by total predictions [OK]

Common Mistakes:

• Confusing accuracy with F1 score
• Thinking accuracy measures only false positives
• Believing accuracy counts number of classes

2. Which of the following is the correct formula for F1 score?

easy

A. Precision + Recall

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

C. True Positives / Total Samples

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

Solution

1. Step 1: Recall F1 score formula

   F1 score is the harmonic mean of precision and recall, calculated as 2 times their product divided by their sum.

2. Step 2: Match formula with options

   2 * (Precision * Recall) / (Precision + Recall) matches the correct formula: 2 * (Precision * Recall) / (Precision + Recall).

3. Final Answer:

   2 * (Precision * Recall) / (Precision + Recall) -> Option B

4. Quick Check:

   F1 = 2PR/(P+R) [OK]

Hint: F1 score = 2 * Precision * Recall / (Precision + Recall) [OK]

Common Mistakes:

• Adding precision and recall instead of harmonic mean
• Using true positives over total samples as F1
• Confusing F1 with specificity

3. Given the confusion matrix below for a binary classifier:

[[50, 10],
 [5, 35]]

What is the accuracy of the model?

medium

A. 75%

B. 70%

C. 90%

D. 85%

Solution

1. Step 1: Identify confusion matrix values

   True Positives (TP) = 50, False Positives (FP) = 10, False Negatives (FN) = 5, True Negatives (TN) = 35.

2. Step 2: Calculate accuracy

   Accuracy = (TP + TN) / (TP + FP + FN + TN) = (50 + 35) / (50 + 10 + 5 + 35) = 85 / 100 = 0.85 or 85%.

3. Final Answer:

   85% -> Option D

4. Quick Check:

   Accuracy = (TP+TN)/Total = 85/100 = 85% [OK]

Hint: Accuracy = (TP + TN) / total samples [OK]

Common Mistakes:

• Adding false positives or false negatives to numerator
• Calculating only TP / total samples
• Mixing up TP and TN values

4. You have this confusion matrix:

[[40, 20],
 [10, 30]]

Which line of code correctly calculates precision for the positive class?

medium

A. precision = TP / (TP + FP)

B. precision = TP / (TP + FN)

C. precision = TN / (TN + FP)

D. precision = TP / (TP + TN)

Solution

1. Step 1: Recall precision formula

   Precision is the ratio of true positives to all predicted positives: TP / (TP + FP).

2. Step 2: Match formula with options

   precision = TP / (TP + FP) correctly uses TP / (TP + FP). precision = TP / (TP + FN) uses recall formula, C and D are incorrect.

3. Final Answer:

   precision = TP / (TP + FP) -> Option A

4. Quick Check:

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

Hint: Precision = true positives / predicted positives [OK]

Common Mistakes:

• Using TP / (TP + FN) which is recall
• Confusing TN with TP in precision
• Dividing by TP + TN instead of TP + FP

5. A model has precision = 0.8 and recall = 0.5. What is the F1 score? Choose the closest value.

hard

A. 0.70

B. 0.65

C. 0.62

D. 0.75

Solution

1. Step 1: Recall F1 score formula

   F1 = 2 * (Precision * Recall) / (Precision + Recall) = 2 * (0.8 * 0.5) / (0.8 + 0.5).

2. Step 2: Calculate F1 score

   Calculate numerator: 2 * 0.4 = 0.8. Calculate denominator: 1.3. F1 = 0.8 / 1.3 ≈ 0.615.

3. Final Answer:

   0.62 -> Option C

4. Quick Check:

   F1 ≈ 0.62 from 0.8 precision and 0.5 recall [OK]

Hint: F1 is harmonic mean: 2PR/(P+R), plug values carefully [OK]

Common Mistakes:

• Averaging precision and recall instead of harmonic mean
• Mixing up precision and recall values
• Rounding too early causing wrong final answer