Practice Variance - 3.5.2 | Module 2: Supervised Learning - Regression & Regularization (Weeks 3) | Machine Learning
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3.5.2 - Variance

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main consequence of high variance in a model?

πŸ’‘ Hint: Think about how a model behaves with lots of complexity.

Question 2

Easy

Define overfitting in your own words.

πŸ’‘ Hint: Consider how a model's training affects its performance elsewhere.

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 high variance in a model indicate?

  • It indicates poor performance.
  • It indicates overfitting.
  • It indicates simplicity.

πŸ’‘ Hint: Consider how a model performs on new data.

Question 2

True or False: A model with high variance will perform consistently well on both training and test data.

  • True
  • False

πŸ’‘ Hint: Think about performance on new vs. known datasets.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are given two datasets: Dataset A is very complex with many outliers, and Dataset B is simple. If you apply a high-degree polynomial model to both datasets and observe the results, discuss how the model's variance would behave differently with each dataset. What could you conclude about model selection?

πŸ’‘ Hint: Consider how noise in the data influences fitting.

Question 2

Design a comprehensive strategy to address overfitting you've observed in a model fit to a dataset. Include approaches related to data, model complexity, and evaluation.

πŸ’‘ Hint: Think broadly about the steps involving the entire modeling process.

Challenge and get performance evaluation