Dear list,

I am trying to use sparklyr ml_cross_validator() function to do the cross validation with machine learning models. It seems the

estimator_param_maps =

argument has to be specified and it is veried for different models. I tried ?ml_cross_validator, it says :

estimator_param_maps A named list of stages and hyper-parameter sets to tune. See details.

whereas the "details" says:


ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.

Is there any example that I can follow on how to set this argument for different models? for instance, people use

estimator_param_maps = list(logistic_regression = list(elastic_net_param = 0)) (see for the logistic regression model.

what is this "elastic_net_param" ? and what would be the parameter to specify here for linear regression, decision tree, random forest, and so on?

Thank you very much.

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