Monte Carlo#

Monte Carlo cross validation or repeated random subsampling is a technique that averages the validation score of a learner over a user-defined number of simulations where the learner is trained and tested on random splits of the dataset.

Interfaces: Validator, Parallel


# Param Default Type Description
1 simulations 10 int The number of simulations i.e. random subsamplings of the dataset.
2 ratio 0.2 float The ratio of samples to hold out for testing.


use Rubix\ML\CrossValidation\MonteCarlo;

$validator = new MonteCarlo(30, 0.1);