Practice Regularization and Optimization - 2.8 | 2. Optimization Methods | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main goal of regularization in machine learning?

πŸ’‘ Hint: Think about how models can either fit too closely or too loosely to training data.

Question 2

Easy

What does L1 regularization encourage in a model?

πŸ’‘ Hint: Consider how this might affect feature selection.

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 L1 regularization aim to promote in a model?

  • A. Dense weights
  • B. Sparsity
  • C. High complexity

πŸ’‘ Hint: It's related to feature selection.

Question 2

True or False: L2 regularization can lead to some of the weights being zero.

  • True
  • False

πŸ’‘ Hint: Think about the mathematical operations involved.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Explain how to choose between L1 and L2 regularization given a dataset with a large number of features. Discuss the criteria you would use to select hyperparameters as well.

πŸ’‘ Hint: Reflect on the properties of the features and performed strategies for hyperparameter tuning.

Question 2

Create a hypothetical scenario demonstrating the impact of setting \(\lambda\) too low and too high in L1 regularization.

πŸ’‘ Hint: Consider the balance between bias and variance and the nature of the data set.

Challenge and get performance evaluation