Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It reduces the coefficients of less important features to zero, promoting sparsity.
B
It increases the complexity of the model by allowing more features to be included.
C
It has no effect on the model's performance but increases computation time.
D
It uniformly scales all coefficients by the same factor.
Understanding the Answer
Let's break down why this is correct
Answer
Increasing the Lasso penalty makes the model prefer smaller coefficient values, so as the penalty grows the coefficients shrink toward zero. The L1 regularization can actually set some coefficients exactly to zero, effectively removing those features from the model. This shrinkage reduces over‑fitting by limiting the model’s flexibility and can improve interpretability because only the most important predictors remain. For example, if a model has coefficients [2, 0. 5, -1.
Detailed Explanation
When the penalty is stronger, the model pushes small coefficients toward zero. Other options are incorrect because Some think a stronger penalty lets more features stay in the model, but actually it removes them; Increasing the penalty does not just waste time; it usually improves accuracy by reducing noise.
Key Concepts
Regularization in predictive models
Lasso regularization
Model complexity
Topic
Regularizers in Predictive Models
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1How does Lasso regression modify the loss function to prevent overfitting in predictive models?
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2
Question 2In the context of predictive modeling, how does the introduction of a penalty term through regularization techniques influence predictive accuracy, particularly in high-dimensional datasets?
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3
Question 3Regularizers such as Lasso and Ridge are used in predictive models to prevent ______ by penalizing complex parameter configurations.
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4
Question 4Which of the following statements about regularizers in predictive models are correct? Select all that apply.
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5
Question 5In what way do regularizers like Lasso and Ridge improve predictive models?
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6
Question 6Ridge regularization : Reduces model complexity :: Lasso regularization : ?
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Question 7How do L1 and L2 regularization contribute to model performance in predictive modeling?
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