Practice Lab: Implementing and Evaluating Logistic Regression and KNN, Interpreting Confusion Matrices - 6 | Module 3: Supervised Learning - Classification Fundamentals (Weeks 5) | Machine Learning
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6 - Lab: Implementing and Evaluating Logistic Regression and KNN, Interpreting Confusion Matrices

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary objective of Logistic Regression?

πŸ’‘ Hint: Think about its name and what it's often used for.

Question 2

Easy

What does K in KNN stand for?

πŸ’‘ Hint: Related to how many 'neighbors' we look at.

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 Logistic Regression predict?

  • Continuous values
  • Binary outcomes
  • Categorical values

πŸ’‘ Hint: Think about its use case in classification.

Question 2

True or False: KNN builds a model during training.

  • True
  • False

πŸ’‘ Hint: Consider the definition of 'training' in predictive modeling.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset with a severe class imbalance. Describe the steps you would take to prepare the data and choose evaluation metrics.

πŸ’‘ Hint: Consider the implications of using accuracy alone.

Question 2

Given that KNN is sensitive to irrelevant features, explain how you'd assess feature relevance before applying KNN.

πŸ’‘ Hint: Think about what constitutes a 'noisy' feature.

Challenge and get performance evaluation