What is the Difference between cross_validate and cross_val_score?

Answer: ‘cross_validate' returns evaluation metrics and other auxiliary information, whereas ‘cross_val_score' returns only the evaluation metrics.

Certainly! Below is a detailed comparison between cross_validate and cross_val_score in the context of scikit-learn:

Feature	cross_validate	cross_val_score
Return Type	Returns a dictionary containing evaluation metrics (e.g., accuracy, precision, recall), training scores, fit times, and test scores for each CV fold.	Returns an array of evaluation metrics (e.g., accuracy, precision, recall) computed for each CV fold.
Flexibility	Provides flexibility to specify multiple evaluation metrics, return train scores, compute fit-times, and capture auxiliary information (e.g., fitted estimators).	Primarily focuses on computing evaluation metrics; less flexible in terms of returning additional information.
Usage	Suitable for scenarios where detailed evaluation metrics, training scores, fit-times, and other information for each CV fold are required.	Suitable for simple cases where only evaluation metrics for each CV fold are needed and additional information is not necessary.
Example	python cross_validate(estimator, X, y, cv=5, scoring=['accuracy', 'precision', 'recall'], return_train_score=True)	python cross_val_score(estimator, X, y, cv=5, scoring='accuracy')

In summary, cross_validate is more comprehensive and flexible, providing detailed information for each CV fold, while cross_val_score is more focused on computing evaluation metrics and is suitable for simpler use cases where additional information is not required. The choice between the two depends on the specific requirements of the evaluation and analysis tasks.