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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Seekh · Accepted Answer

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.

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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