Practice - Regularization and Optimization
Practice Questions
Test your understanding with targeted questions
What is the main goal of regularization in machine learning?
💡 Hint: Think about how models can either fit too closely or too loosely to training data.
What does L1 regularization encourage in a model?
💡 Hint: Consider how this might affect feature selection.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does L1 regularization aim to promote in a model?
💡 Hint: It's related to feature selection.
True or False: L2 regularization can lead to some of the weights being zero.
💡 Hint: Think about the mathematical operations involved.
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Challenge Problems
Push your limits with advanced challenges
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.
💡 Hint: Reflect on the properties of the features and performed strategies for hyperparameter tuning.
Create a hypothetical scenario demonstrating the impact of setting \(\lambda\) too low and too high in L1 regularization.
💡 Hint: Consider the balance between bias and variance and the nature of the data set.
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Reference links
Supplementary resources to enhance your learning experience.