📚 Learning Guide
Regularizers in Predictive Models
easy

What is the primary purpose of using regularizers in predictive models?

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Choose 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

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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

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