Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
To increase the complexity of the model
B
To prevent overfitting by adjusting the loss function
C
To decrease the amount of data needed for training
D
To speed up the training process
Understanding the Answer
Let's break down why this is correct
Answer
The primary purpose of regularizers in predictive models is to prevent overfitting by discouraging overly complex or large model coefficients. They add a penalty term to the training loss that grows with the size or complexity of the model, forcing the algorithm to keep weights small or sparse. This keeps the model general enough to perform well on new data, because it reduces reliance on noise in the training set. For example, in a linear regression that fits 100 features, adding an L2 penalty makes the coefficients shrink, so the model uses only the most important predictors instead of fitting every tiny fluctuation.
Detailed Explanation
Regularizers add a penalty to the loss function, which discourages large weights. Other options are incorrect because People sometimes think regularizers make the model more complex, but they actually do the opposite; Regularizers do not reduce the amount of data needed.
Key Concepts
loss function adjustment
Topic
Regularizers in Predictive Models
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1How do penalty terms in regularization techniques assist in feature selection within predictive models?
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2
Question 2Which of the following statements about regularizers in predictive models are correct? Select all that apply.
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3
Question 3In what way do regularizers like Lasso and Ridge improve predictive models?
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4
Question 4How do L1 and L2 regularization contribute to model performance in predictive modeling?
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