Computer scienceData scienceMachine learningIntroduction to machine learning

Cross-validation

Train-validation-test and cross-validation splits

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What are the key differences between using train-validation-test split and cross-validation?

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Cross-validation is a more robust technique for evaluating the performance of a machine learning model on imbalanced data than train-validation-test

Train-validation-test split is always more time-consuming than cross-validation

Cross-validation can lead to better generalization when compared to train-validation-test split by using different subsets of data for training and validation

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