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3.5 Train-Test Split and Cross-Validation

In the realm of machine learning, it is crucial to accurately gauge a model's ability to generalize to new, unseen data. This evaluation process helps identify and mitigate one of the most prevalent challenges in the field: overfitting. Overfitting occurs when a model becomes excessively tailored to the training data, performing exceptionally well on familiar examples but struggling to maintain that performance on novel instances. To combat this issue and ensure robust model performance, data scientists employ two primary techniques: train-test split and cross-validation.

These methodologies serve as cornerstones in the assessment of model performance, providing valuable insights into a model's capacity ...