Practice Confusion Matrix - 12.2 | 12. Evaluation Methodologies of AI Models | CBSE Class 12th AI (Artificial Intelligence)
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12.2 - Confusion Matrix

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does TP stand for in a confusion matrix?

💡 Hint: Think about what it means when the prediction is correct for the positive class.

Question 2

Easy

What is the purpose of a confusion matrix?

💡 Hint: Consider what characteristics are compared.

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 FP stand for in the context of confusion matrix?

  • True Positive
  • False Positive
  • True Negative

💡 Hint: Think about what happens when the model makes a mistake in identifying positives.

Question 2

True or False: In the confusion matrix, true negatives indicate correct positive predictions.

  • True
  • False

💡 Hint: Recall the definitions of true positives and true negatives.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a confusion matrix with the following results: TP = 75, TN = 50, FP = 25, FN = 10. Calculate the Precision, Recall, and Accuracy.

💡 Hint: Use the definitions for each metric and apply them based on the confusion matrix values.

Question 2

In a real-world scenario, if your model has many false positives yet high accuracy, what does this indicate about the dataset and the model's performance?

💡 Hint: Consider how accuracy alone can be misleading in evaluating model performance.

Challenge and get performance evaluation