Explanation:
Model validation in AutoML involves assessing the model's performance on unseen data to ensure it generalizes well to new instances. Cross-validation is a critical technique for this, as it partitions the data into multiple folds and trains the model on different subsets, reducing the risk of overfitting. Examples of cross-validation techniques used in AutoML include leave-one-out cross-validation, where each data point is used as a validation set once, bootstrap sampling, which involves resampling with replacement, and repeated random sub-sampling, which randomly splits the data into training and validation sets multiple times.
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Describe the process of model validation in AutoML. Discuss the importance of cross-validation and how it helps in assessing the generalization ability of a model. Provide examples of cross-validation techniques used in AutoML.
A
Holdout method, k-fold cross-validation, and stratified sampling.
B
Leave-one-out cross-validation, bootstrap sampling, and repeated random sub-sampling.
C
Monte Carlo cross-validation, nested cross-validation, and time series split.
D
Block cross-validation, group cross-validation, and permutation tests.