Practice Why Model Evaluation is Important - 28.1 | 28. Introduction to Model Evaluation | CBSE Class 10th AI (Artificial Intelleigence)
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

28.1 - Why Model Evaluation is Important

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 is model evaluation?

💡 Hint: Think about why testing is important in any discipline.

Question 2

Easy

Name one reason why avoiding overfitting is essential in machine learning.

💡 Hint: Focus on what happens when a model learns too closely from the training data.

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 is the role of model evaluation?

  • To train the model
  • To check model performance
  • To increase model complexity

💡 Hint: Think about the reason we test anything after learning.

Question 2

True or False: Overfitting occurs when a model performs well on training data but poorly on new data.

  • True
  • False

💡 Hint: Recall what we discussed about memorization.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Imagine a financial institution has a model with an accuracy of 85% but fails to predict 30% of actual defaults due to an imbalanced dataset. What steps can the institution take to improve this model?

💡 Hint: Think about how to address data shortcomings.

Question 2

You are developing a spam filter that classifies emails. You find that while your model has high accuracy, it's misclassifying important emails as spam frequently. What performance metrics would you analyze, and what actions would you take?

💡 Hint: Recall the significance of precision in spam classification.

Challenge and get performance evaluation