IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

The purpose of using regularization techniques in machine learning is primarily to prevent overfitting by adding a penalty for larger coefficients in the model. Overfitting occurs when a model learns the noise and details in the training data rather than just the underlying trends. This can lead to a model that performs well on training data but poorly on unseen or test data.

Regularization methods, such as L1 (Lasso) and L2 (Ridge) regularization, introduce a constraint on the size of the coefficients. By penalizing larger coefficients, regularization encourages the model to be simpler and more generalizable. This penalty helps in maintaining a balance between fitting the training data well and keeping the model complexity in check. As a result, regularization techniques not only improve the model's predictive performance on new data but also promote the selection of features that contribute meaningfully to the prediction, leading to a more interpretable and efficient model.