Practice Confusion Matrix - 8.2 | Chapter 8: Model Evaluation Metrics | Machine Learning Basics
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8.2 - 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 the context of a Confusion Matrix?

💡 Hint: Think about what is correctly identified.

Question 2

Easy

What information does a Confusion Matrix display?

💡 Hint: Recall the four components of the matrix.

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 FN stand for in the context of a Confusion Matrix?

  • True Negative
  • False Negative
  • True Positive

💡 Hint: Think about what happens when a positive case is missed.

Question 2

True or False: A higher number of False Positives is always good for a model's performance.

  • True
  • False

💡 Hint: Reflect on the consequences of misclassifying.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

A model predicts whether a student will pass or fail the exam. In a class of 30 students, 20 passed (TP), 5 failed but were predicted to pass (FP), 3 were predicted to fail but actually passed (FN), and 2 accurately failed (TN). Construct the confusion matrix and calculate the accuracy.

💡 Hint: First, create the matrix, then use the formula to find the accuracy.

Question 2

Consider a model that predicts whether emails are spam or not. If the model flags 70 spam emails correctly (TP), 30 emails incorrectly as spam (FP), and misses 10 actual spam emails (FN) while marking 90 genuine emails as not spam (TN), analyze the model's performance and suggest improvements.

💡 Hint: Focus on the performance metrics and how they can be improved.

Challenge and get performance evaluation