Confusion matrix in ML Python

Introduction

A confusion matrix helps us see how well a model is doing by showing where it gets things right and where it makes mistakes.

Checking how well a model classifies emails as spam or not spam.

Evaluating a model that predicts if a patient has a disease or not.

Understanding errors in a model that recognizes handwritten digits.

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

Syntax

ML Python

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(true_labels, predicted_labels)

true_labels are the actual correct answers.

predicted_labels are what the model guessed.

Examples

This example shows a simple confusion matrix for two classes: 0 and 1.

ML Python

from sklearn.metrics import confusion_matrix

true_labels = [0, 1, 0, 1]
predicted_labels = [0, 0, 0, 1]
cm = confusion_matrix(true_labels, predicted_labels)
print(cm)

Here, we specify class order with labels for clarity.

ML Python

from sklearn.metrics import confusion_matrix

true_labels = ['cat', 'dog', 'cat', 'dog']
predicted_labels = ['dog', 'dog', 'cat', 'cat']
cm = confusion_matrix(true_labels, predicted_labels, labels=['cat', 'dog'])
print(cm)

Sample Model

This program compares true labels and predicted labels for a binary classification. It prints the confusion matrix showing counts of correct and incorrect predictions.

ML Python

from sklearn.metrics import confusion_matrix

# Actual labels
true_labels = [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]

# Model predictions
predicted_labels = [1, 0, 0, 1, 0, 1, 1, 0, 1, 0]

# Calculate confusion matrix
cm = confusion_matrix(true_labels, predicted_labels)

print("Confusion Matrix:")
print(cm)

Important Notes

The confusion matrix rows represent actual classes, and columns represent predicted classes.

Diagonal values show correct predictions; off-diagonal values show mistakes.

It works for any number of classes, not just two.

Summary

A confusion matrix shows how many times a model guessed each class correctly or incorrectly.

It helps understand model errors clearly.

Use it to improve and compare classification models.

Practice

1.

What does a confusion matrix primarily show in machine learning?

easy

A. The number of correct and incorrect predictions for each class

B. The speed of the model training process

C. The size of the dataset used for training

D. The number of features in the dataset

Solution

1. Step 1: Understand the purpose of a confusion matrix

   A confusion matrix shows how many times a model predicted each class correctly or incorrectly.

2. Step 2: Identify what it displays

   It displays counts of true positives, true negatives, false positives, and false negatives for each class.

3. Final Answer:

   The number of correct and incorrect predictions for each class -> Option A

4. Quick Check:

   Confusion matrix = correct and incorrect counts [OK]

Hint: Confusion matrix counts correct and wrong guesses per class [OK]

Common Mistakes:

• Thinking it shows training speed
• Confusing it with dataset size
• Assuming it shows feature count

2.

Which of the following is the correct way to represent a confusion matrix for a binary classifier?

[[TP, FP],
 [FN, TN]]

easy

A. true Positives, false Positives in first row; false Negatives, true Negatives in second row

B. true Negatives, false Negatives in first row; false Positives, true Positives in second row

C. false Positives, true Positives in first row; true Negatives, false Negatives in second row

D. false Negatives, true Negatives in first row; true Positives, false Positives in second row

Solution

1. Step 1: Recall confusion matrix layout for binary classification

   The standard layout is: first row has true Positives (TP) and false Positives (FP), second row has false Negatives (FN) and true Negatives (TN).

2. Step 2: Match the given matrix to the layout

   The given matrix [[TP, FP], [FN, TN]] matches the standard layout exactly.

3. Final Answer:

   true Positives, false Positives in first row; false Negatives, true Negatives in second row -> Option A

4. Quick Check:

   Binary confusion matrix = [[TP, FP], [FN, TN]] [OK]

Hint: Remember TP and FP are in the first row [OK]

Common Mistakes:

• Swapping rows and columns
• Confusing FP with FN positions
• Mixing up TP and TN placement

3.

Given the confusion matrix for a 3-class classifier:

[[5, 2, 0],
 [1, 7, 1],
 [0, 2, 6]]

What is the total number of correct predictions?

medium

A. 22

B. 20

C. 21

D. 18

Solution

1. Step 1: Identify correct predictions in confusion matrix

   Correct predictions are on the diagonal: 5 (class 1), 7 (class 2), and 6 (class 3).

2. Step 2: Sum the diagonal values

   5 + 7 + 6 = 18 correct predictions in total.

3. Final Answer:

   18 -> Option D

4. Quick Check:

   Sum diagonal = 18 correct predictions [OK]

Hint: Sum diagonal elements for correct predictions [OK]

Common Mistakes:

• Summing all matrix elements instead of diagonal
• Adding off-diagonal values by mistake
• Ignoring one of the diagonal values

4.

Consider this confusion matrix for a binary classifier:

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

There is a mistake in interpreting the false Positives (FP). Which is the correct FP count?

medium

A. 50

B. 5

C. 10

D. 35

Solution

1. Step 1: Recall FP position in confusion matrix

   In a binary confusion matrix [[TP, FP], [FN, TN]], FP is at position [0][1].

2. Step 2: Identify FP value in given matrix

   Value at [0][1] is 10, so FP = 10.

3. Final Answer:

   10 -> Option C

4. Quick Check:

   FP = matrix[0][1] = 10 [OK]

Hint: FP is always top-right in binary confusion matrix [OK]

Common Mistakes:

• Confusing FP with FN
• Taking FP as bottom-left value
• Mixing FP with TN or TP

5.

A model's confusion matrix for a 3-class problem is:

[[40, 5, 0],
 [3, 30, 7],
 [0, 2, 38]]

Calculate the overall accuracy of the model.

hard

A. 0.82

B. 0.85

C. 0.88

D. 0.90

Solution

1. Step 1: Calculate total correct predictions

   Sum diagonal: 40 + 30 + 38 = 108 correct predictions.

2. Step 2: Calculate total predictions

   Sum all elements: (40+5+0) + (3+30+7) + (0+2+38) = 45 + 40 + 40 = 125 total predictions.

3. Step 3: Compute accuracy

   Accuracy = correct / total = 108 / 125 = 0.864, rounded to 0.86.

4. Final Answer:

   0.86 -> Option B

5. Quick Check:

   Accuracy = 108/125 ≈ 0.86 [OK]

Hint: Accuracy = sum diagonal / sum all elements [OK]

Common Mistakes:

• Using only one class's correct count
• Forgetting to sum all matrix elements
• Rounding incorrectly