IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Assessing model performance with cross-validation involves the process of partitioning the training set into complementary subsets, which allows the model to be trained and validated multiple times. This method enhances the robustness of the evaluation by ensuring that the model is tested against different subsets of data, providing a more reliable estimate of its performance.

With cross-validation, typically in k-fold cross-validation, the dataset is divided into k subsets (or folds). The model is trained k times, each time leaving out one of the subsets for validation while the remaining k-1 subsets are used for training. This results in multiple assessments of the model's performance, and the overall performance can be averaged across all iterations to give a more comprehensive view of how the model generalizes to unseen data.

This approach helps in mitigating issues related to overfitting, as it evaluates the model on various segments of the data rather than relying on a single test split. Overall, option B provides a method that ensures a thorough analysis of a model’s predictive capability, making it the correct choice.