Practice Evaluation - 1.4.6 | Introduction to Data Science | Data Science Basic
K12 Students

Academics

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

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

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

Professional Courses
Games

Interactive Games

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

games
Typing
Memory
Math
English Adventures
Knowledge

1.4.6 - Evaluation

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 mock test.

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define accuracy in the context of model evaluation.

💡 Hint: Think about how you would measure performance overall.

Question 2

Easy

What does precision measure?

💡 Hint: Focus on what percentage of positive predictions were actually 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 is the primary focus of precision in model evaluation?

  • The correctness of positive predictions
  • The number of true positives
  • Overall performance

💡 Hint: Think about how many of the positive predictions are right.

Question 2

True or False: Recall is more important than precision when missing a positive case has severe consequences.

  • True
  • False

💡 Hint: Consider examples like disease detection.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a predictive model that classifies emails as spam or not. Out of 100 emails, 40 were identified as spam, but 10 of these were not spam. Also, there are 20 spam emails that were not identified by the model. Calculate the accuracy, precision, recall, and F1 score of the model.

💡 Hint: Apply the formulas for accuracy, precision, and recall to find the F1 score.

Question 2

In a given dataset, you are asked to evaluate two models. Model A yields a precision of 90% and a recall of 60%. Model B presents with precision of 80% and recall of 80%. Which model would you recommend, and why?

💡 Hint: Evaluate if you prioritize correctness in alerts or capturing all relevant cases.

Challenge and get performance evaluation