Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
They reduce overfitting by penalizing complex models.
B
They increase model complexity to enhance performance.
C
They eliminate the need for feature selection entirely.
D
They ensure that all parameters are non-zero.
Understanding the Answer
Let's break down why this is correct
Answer
Regularizers such as Lasso and Ridge add a penalty to the loss function that discourages large coefficient values, which makes the model less sensitive to noise and reduces overfitting. They do this by shrinking the coefficients toward zero—Lasso can set some exactly to zero, creating a simpler model, while Ridge shrinks them continuously, keeping all features but with smaller influence. This shrinkage limits the model’s complexity, so it generalizes better to new data. For example, if a dataset has 20 predictors but only a few truly affect the outcome, Lasso will drop the irrelevant ones, leaving a clearer, more accurate predictive model.
Detailed Explanation
Regularizers add a small extra cost that grows with the size of the coefficients. Other options are incorrect because The idea that making the model more complex helps it learn better is a common mistake; Some people think that regularizers replace the need to pick which features to use.
Key Concepts
Regularization in predictive modeling
Overfitting in machine learning
Feature selection techniques
Topic
Regularizers in Predictive Models
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of parametrized predictors, which combination of estimation techniques and regularization methods can lead to improved model evaluation by reducing overfitting?
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2
Question 2What is the primary purpose of using regularizers 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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4
Question 4Regularizers such as Lasso and Ridge are used in predictive models to prevent ______ by penalizing complex parameter configurations.
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5
Question 5Which of the following statements about regularizers in predictive models are correct? Select all that apply.
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6
Question 6A data scientist is working on a predictive model to forecast housing prices. They notice that the model tends to overfit the training data, leading to poor performance on unseen data. To address this issue, they decide to implement regularization. Which of the following approaches would best help them reduce overfitting while maintaining model interpretability?
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7
Question 7What effect does increasing the strength of Lasso regularization (`1) have on a predictive model's coefficients?
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8
Question 8Ridge regularization : Reduces model complexity :: Lasso regularization : ?
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9
Question 9How do L1 and L2 regularization contribute to model performance in predictive modeling?
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