Practice Metrics - 4.2 | Classification Algorithms | 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

4.2 - Metrics

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

What does TP stand for in the context of a confusion matrix?

💡 Hint: Think about how many times we correctly predicted the positive class.

Question 2

Easy

How would you calculate accuracy from a confusion matrix?

💡 Hint: Remember to add true positives and true negatives.

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?

  • The model predicted a negative case correctly
  • The model predicted a positive case correctly
  • The model predicted all cases as positive

💡 Hint: Think about what the 'True' refers to in this context.

Question 2

Is a low F1-score an indication of poor model performance?

  • True
  • False

💡 Hint: Consider the relationship between precision and recall.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset where 100 instances were tested. The confusion matrix shows TP=70, FP=10, TN=15, FN=5. What are the accuracy, precision, recall, and F1-score?

💡 Hint: Break down each metric using the confusion matrix values.

Question 2

Propose a strategy to increase the precision of a model that has low precision currently while ensuring not to severely sacrifice recall.

💡 Hint: Think about what kind of adjustments help in precision without completely missing too many actual positive cases.

Challenge and get performance evaluation