Practice Evaluating Classification Models - 4 | Classification Algorithms | Data Science Basic
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

Typing
Memory
Math
English Adventures
Knowledge

4 - Evaluating Classification Models

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does True Positive mean in a confusion matrix?

💡 Hint: Think about what 'true' indicates in terms of predictions.

Question 2

Easy

Define Accuracy in terms of classification performance.

💡 Hint: It's a simple formula involving TP and TN.

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 True Positive indicate in the context of a confusion matrix?

  • Correctly predicted negative instances
  • Correctly predicted positive instances
  • Incorrectly predicted positive instances

💡 Hint: Focus on what 'True' and 'Positive' mean in this context.

Question 2

True or False: F1-Score is calculated as the average of Precision and Recall.

  • True
  • False

💡 Hint: Recall the specific formula for F1-Score.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a confusion matrix showing TP=90, FP=30, TN=50, and FN=10. Discuss the implications of these values on model performance and calculate the Precision, Recall, and F1-Score.

💡 Hint: Apply formulas directly and consider real-world consequences.

Question 2

Consider a scenario where the confusion matrix reveals a high accuracy but low recall. What might this indicate about the model, and how could it be adjusted?

💡 Hint: Backtrack through your definitions of precision and recall to connect them.

Challenge and get performance evaluation