Practice Summary - 8 | Chapter 7: Supervised Learning – Logistic Regression | Machine Learning Basics
K12 Students

Academics

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

Academics
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

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What type of outcome does logistic regression deal with?

💡 Hint: Think about how answers can be only two alternatives.

Question 2

Easy

Define the sigmoid function.

💡 Hint: Can you remember how it looks mathematically?

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 the sigmoid function output?

  • Probability between 0 and 1
  • A categorical label
  • Linear values

💡 Hint: Remember its purpose in logistic regression.

Question 2

True or False: Logistic regression is used for multi-class classification.

  • True
  • False

💡 Hint: Think about the definition of logistics and its category handling.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a dataset with 1000 observations where 800 are positive and 200 are negative. If a logistic regression model predicts 750 negatives and 250 positives, calculate accuracy.

💡 Hint: Use the definition of accuracy based on true positives and true negatives.

Question 2

Given a logistic regression model output of 0.65 for a sample, if the threshold is set at 0.7, what will the classification be?

💡 Hint: Think about how classifications depend on the threshold.

Challenge and get performance evaluation