Practice Confusion Matrix for Multi-Class Classification - 30.6 | 30. Confusion Matrix | CBSE Class 10th AI (Artificial Intelleigence)
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30.6 - Confusion Matrix for Multi-Class Classification

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a confusion matrix?

💡 Hint: Think about how classification results are organized.

Question 2

Easy

Explain what True Positives represent in a confusion matrix.

💡 Hint: Consider what it means for a prediction to be correct.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does a confusion matrix help with?

  • Understanding model performance
  • Visualizing input data
  • Data preprocessing
  • Feature engineering

💡 Hint: Consider the purpose of evaluating a model.

Question 2

True or False? The diagonal values in a confusion matrix represent misclassifications.

  • True
  • False

💡 Hint: Think about what the diagonal signifies.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a confusion matrix for a multi-class classification problem with the following counts: Actual Positives for classes A, B, and C are 50, 60, and 40 respectively, and the predicted counts are as follows: Class A (TP: 40, FP: 10, FN: 10), Class B (TP: 45, FP: 5, FN: 15), Class C (TP: 25, FP: 5, FN: 20). Calculate the overall accuracy, precision, recall, and F1 score for each class.

💡 Hint: Calculate each metric step-by-step for clarity.

Question 2

If you have the following confusion matrix for a multi-class problem:
| | Predicted A | Predicted B | Predicted C |
|------------|--------------|--------------|--------------|
| Actual A | 30 | 10 | 5 |
| Actual B | 2 | 25 | 3 |
| Actual C | 1 | 3 | 50 |
Identify where the most significant errors are happening and propose potential fixes.

💡 Hint: Look for patterns in the prediction errors to suggest improvements.

Challenge and get performance evaluation