Practice Logistic Regression & K-nearest Neighbors (knn) (5) - Supervised Learning - Classification Fundamentals (Weeks 5)
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Logistic Regression & K-Nearest Neighbors (KNN)

Practice - Logistic Regression & K-Nearest Neighbors (KNN)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary goal of a classification algorithm?

💡 Hint: Think about what classification tries to achieve.

Question 2 Easy

What does logistic regression predict?

💡 Hint: What type of output do we get from logistic regression?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is logistic regression primarily used for?

Regression analysis
Classification
Clustering

💡 Hint: Think about whether we are dealing with categories.

Question 2

True or False: KNN builds a model during the training phase.

True
False

💡 Hint: Consider what KNN does with its data in training.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are given a class-imbalanced dataset with 95% negatives and 5% positives. Discuss how you would evaluate a logistic regression model trained on this data.

💡 Hint: What might you do if accuracy doesn't reflect true performance?

Challenge 2 Hard

Given a dataset with features that can lead to high dimensionality, propose a method to reduce dimensionality before applying KNN.

💡 Hint: Consider methods that help retain the most significant features.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.