Infer.NET user guide : Learners : Bayes Point Machine classifiers : Command-line runners
You can use the
CrossValidate module to assess the generalization performance of the Bayes Point Machine, both in binary and multi-class classification. The
CrossValidate module starts by reading a labelled data from a file and partitions its instances into subsets of equal size, known as folds. It then trains the Bayes Point Machine classifier on - 1 folds and evaluates its performance on the withheld -th fold. It cycles through all combinations of splits into training and validation sets to finally report the overall performance results.
CrossValidate module has the following command-line arguments:
data-set: The file containing ground truth labels and features in the format described earlier.
results: The CSV file to which the cross-validation results will be saved.
folds: The number of cross-validation folds to use (defaults to 5).
iterations: The number of training algorithm iterations (defaults to 30).
batches: The number of batches into which the training data is split (defaults to 1).
compute-evidence: If specified, the Bayes Point Machine classifier will compute model evidence on the training data (defaults to false).
For more information about the command-line arguments, see Settings.
Learner Classifier BinaryBayesPointMachine CrossValidate --data-set training.dat --results cross-validation-results.csv --iterations 15 --batches 1 --compute-evidence Learner Classifier MulticlassBayesPointMachine CrossValidate --data-set training.dat --results cross-validation-results.csv --iterations 15 --batches 1 --compute-evidence