What is the purpose of the cross-validation set?
The goal of cross-validation is to estimate the expected level of fit of a model to a data set that is independent of the data that were used to train the model. It can be used to estimate any quantitative measure of fit that is appropriate for the data and model.
Is cross-validation an ensemble technique?
Ensemble learning are methods that combine the predictions from multiple models. The models used in this estimation process can be combined in what is referred to as a resampling-based ensemble, such as a cross-validation ensemble or a bootstrap aggregation (or bagging) ensemble.
How is a cross validated model built in H2O?
With cross-validated model building, H2O builds K+1 models: K cross-validated model and 1 overarching model over all of the training data. Each cv-model produces a prediction frame pertaining to its fold. It can be saved and probed from the various clients if keep_cross_validation_predictions parameter is set in the model constructor.
When to use k-fold cross validation in H2O?
K-fold cross-validation is used to validate a model internally, i.e., estimate the model performance without having to sacrifice a validation split. Also, you avoid statistical issues with your validation split (it might be a “lucky” split, especially for imbalanced data).
How is stacked ensemble used in H2O algorithms?
H2O’s Stacked Ensemble method is a supervised ensemble machine learning algorithm that finds the optimal combination of a collection of prediction algorithms using a process called stacking. like all supervised models in H2O, Stacked Ensemble supports regression, binary classification, and multiclass classification.
How are holdout predictions scored in cross validation?
This “holdout prediction” is then scored against the true labels, and the overall cross-validation metrics are computed. This approach has some implications. Scoring the holdout predictions freshly can result in different metrics than taking the average of the 5 validation metrics of the cross-validation models.