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Regularizers in Predictive Models
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A 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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A

Use Lasso regularization to promote sparsity in the model coefficients

B

Increase the number of features in the model to capture more complexity

C

Apply no regularization and rely on cross-validation for performance assessment

D

Use a more complex model to better fit the training data

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Using L1 regularization shrinks the size of each coefficient toward zero. Other options are incorrect because Adding more features can give the model more ways to fit random noise; Skipping regularization relies only on cross‑validation to judge performance.

Key Concepts

Regularization in predictive models
Overfitting in machine learning
Model interpretability
Topic

Regularizers in Predictive Models

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medium level question

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understand

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Definition
Definition

Regularizers are functions that control the sensitivity of predictive models by penalizing complex or sensitive parameter configurations. Common regularizers include `2 (ridge) and `1 (Lasso) regularization, which encourage stable and sparse parameter solutions.

Topic Definition

Regularizers are functions that control the sensitivity of predictive models by penalizing complex or sensitive parameter configurations. Common regularizers include `2 (ridge) and `1 (Lasso) regularization, which encourage stable and sparse parameter solutions.

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