In 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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It always decreases predictive accuracy by adding noise to the model.

It can improve predictive accuracy by preventing overfitting in high-dimensional datasets.

It has no effect on predictive accuracy regardless of the dataset dimensions.

It only affects the model training time without influencing accuracy.

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Adding a penalty term to a predictive model shrinks the estimated coefficients, which helps prevent the model from fitting noise in the data. In high‑dimensional settings where the number of features can far exceed the number of observations, this shrinkage reduces the model’s variance and improves its ability to generalize. The penalty forces many weights toward zero or small values, trading a little bias for a large reduction in variance. For example, applying Lasso to a dataset with 1,000 features and only 50 observations can drive most coefficients to zero, leaving only the most predictive variables and yielding a lower test‑set error. Thus, regularization improves predictive accuracy by controlling model complexity, especially when p is much larger than n.

Detailed Explanation

Adding a penalty term tells the model to keep its numbers small. Other options are incorrect because The idea that a penalty adds noise is wrong; Saying the penalty has no effect ignores how it changes the model’s behavior.

Key Concepts

regularization

penalty term

predictive accuracy

Topic

Regularizers in Predictive Models

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hard level question

Cognitive Level

understand

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