IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

The main goal of regularization in machine learning is to prevent overfitting in models. Overfitting occurs when a model captures noise or random fluctuations in the training data to the extent that it negatively impacts its performance on unseen data. Regularization techniques, such as Lasso and Ridge regularization, add a penalty to the loss function that discourages overly complex models by reducing the influence of less important features. This helps create a model that generalizes better to new data, thus improving its predictive performance.

In contrast, the other options do not accurately represent the purpose of regularization. Enhancing data clustering approaches or visualization efforts does not align with the primary aim of ensuring that the model performs well beyond the dataset it was trained on. Similarly, while regularization can indirectly affect a model’s training speed by potentially simplifying the model, its main focus is on improving generalization rather than speeding up training.