This function trains a model fit by owl() by tuning its parameters
through cross-validation.
trainOwl( x, y, q = 0.2, number = 10, repeats = 1, measure = c("mse", "mae", "deviance", "missclass", "auc"), cl = NULL, ... )
| x | the feature matrix, which can be either a dense matrix of the standard matrix class, or a sparse matrix inheriting from Matrix::sparseMatrix Data frames will be converted to matrices internally. |
|---|---|
| y | the response. For Gaussian models this must be numeric; for binomial models, it can be a factor. |
| q | shape of lambda sequence |
| number | number of folds (cross-validation) |
| repeats | number of repeats for each fold (for repeated k-fold cross validation) |
| measure | measure to try to optimize; note that you may supply multiple values here and that, by default, all the possible measures for the given model will be used. |
| cl | cluster if parallel fitting is desired. Can be any
cluster accepted by |
| ... | other arguments to pass on to |
An object of class "TrainedOwl", with the following slots:
a summary of the results with means, standard errors, and 0.95 confidence levels
the raw data from the model training
a data.frame of the best (mean) values for the different metrics and their corresponding parameter values
a data.frame listing the used metrics and their labels
the model fit to the entire data set
the call
Note that by default this method matches all of the available metrics
for the given model family against those provided in the argument
measure. Collecting these measures is not particularly demanding
computationally so it is almost always best to leave this argument
as it is and then choose which argument to focus on in the call
to plot.TrainedOwl().