Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.
Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβperfect for learners of all ages.
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the main goal of regularization in machine learning?
π‘ 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?
π‘ Hint: Consider how this might affect feature selection.
Practice 4 more questions and get performance evaluation
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?
π‘ Hint: It's related to feature selection.
Question 2
True or False: L2 regularization can lead to some of the weights being zero.
π‘ Hint: Think about the mathematical operations involved.
Solve and get performance evaluation
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.
π‘ 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.
π‘ Hint: Consider the balance between bias and variance and the nature of the data set.
Challenge and get performance evaluation