Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
By increasing the model complexity
B
By shrinking coefficients of less important features to zero
C
By ensuring all features are included regardless of their relevance
D
By increasing the training data size
Understanding the Answer
Let's break down why this is correct
Answer
Penalty terms in regularization add a cost for using many or large coefficients, so the model prefers simpler explanations. Because each coefficient’s size is penalized, the optimization will shrink some coefficients toward zero; the ones that stay non‑zero are the most useful predictors. This automatic shrinking effectively drops irrelevant features, acting as a built‑in feature selector. For example, in Lasso regression a coefficient that contributes little to predicting the outcome is forced to zero, so that feature is removed from the final model. Thus, regularizers help keep models accurate while keeping only the most important features.
Detailed Explanation
Penalty terms act like a gentle hand that pulls small feature weights toward zero. Other options are incorrect because Some think the penalty makes the model more complex, but it actually does the opposite; A common mistake is to think the penalty forces every feature to stay in the model.
Key Concepts
penalty term
feature selection
Topic
Regularizers in Predictive Models
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1What is the primary purpose of using regularizers in predictive models?
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Question 2How does the introduction of a penalty term in regularization affect model complexity and the process of hyperparameter tuning in predictive models?
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3
Question 3In 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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Question 4How do L1 and L2 regularization contribute to model performance in predictive modeling?
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