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. The estimated validation score approaches the actual validation score as the number of simulations goes to infinity, however, only a tiny fraction of all possible simulations are needed to produce a pretty good approximation.
|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);