Answer
Split data into multiple folds.
Train and test repeatedly.
Ensures robust model evaluation.
Train and test repeatedly.
Ensures robust model evaluation.
Quick Answer
Cross-validation evaluates model performance more reliably than single train/test split. KFold: split data into K folds, train on K-1, test on 1, repeat K times, average scores. StratifiedKFold for classification (preserves class distribution in each fold). cross_val_score(model, X, y, cv=5) does 5-fold CV. Use for: hyperparameter tuning (GridSearchCV uses CV internally), comparing models, reporting performance.
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SugharaIQ Editorial Team
Verified Answer
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Source: SugharaIQ