Réponse
The crossValidate action supports several model types, including 'BNET' (Bayesian Network Classifier), 'DECISIONTREE', 'FACTMAC' (Factorization Machine), 'FOREST', 'GRADBOOST' (Gradient Boosting Tree), 'NEURALNET', and 'SVM' (Support Vector Machine).
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Other questions about crossValidate
How can I manage the randomness of fold sampling?
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What is the purpose of the crossValidate action?
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How can I control the training process within cross-validation?
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What is the function of the 'kFolds' parameter?
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Is it possible to run the cross-validation folds in parallel?
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