Learning Path
Question & Answer1
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Explore TopicChoose the Best Answer
A
Increases model bias
B
Encourages parameter sparsity
C
Eliminates all parameters
D
Avoids overfitting
Understanding the Answer
Let's break down why this is correct
Answer
Ridge regularization shrinks all coefficients toward zero, making the model simpler but keeping every feature. Lasso regularization does the same but also pushes many coefficients exactly to zero, which removes those features from the model entirely. This happens because Lasso’s penalty is based on the absolute value of coefficients, encouraging sparsity. For example, if a model has five predictors, Lasso might set two of them to zero, leaving only three active predictors. Thus, Lasso regularization reduces model complexity by eliminating irrelevant features.
Detailed Explanation
Lasso adds a penalty that pushes many coefficients to exactly zero. Other options are incorrect because The idea that Lasso automatically increases bias is a misunderstanding; Lasso does not delete every parameter.
Key Concepts
Regularization in predictive models
Model complexity and bias-variance tradeoff
Parameter tuning
Topic
Regularizers in Predictive Models
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Regularizers such as Lasso and Ridge are used in predictive models to prevent ______ by penalizing complex parameter configurations.
easyComputer-science
Practice
2
Question 2In what way do regularizers like Lasso and Ridge improve predictive models?
easyComputer-science
Practice
3
Question 3What effect does increasing the strength of Lasso regularization (`1) have on a predictive model's coefficients?
mediumComputer-science
Practice
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