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Grid Search#

Grid Search is an algorithm that optimizes hyper-parameter selection. From the user's perspective, the process of training and predicting is the same, however, under the hood Grid Search trains a model for each combination of possible parameters and the best model is selected as the base estimator.

Interfaces: Estimator, Learner, Parallel, Persistable, Verbose

Data Type Compatibility: Depends on base learner


# Name Default Type Description
1 base string The class name of the base learner.
2 params array An array of lists containing the possible values for each of the base learner's constructor parameters.
3 metric auto Metric The validation metric used to score each set of hyper-parameters.
4 validator KFold Validator The validator used to test and score the model.


use Rubix\ML\GridSearch;
use Rubix\ML\Classifiers\KNearestNeighbors;
use Rubix\ML\Kernels\Distance\Euclidean;
use Rubix\ML\Kernels\Distance\Manhattan;
use Rubix\ML\CrossValidation\Metrics\FBeta;
use Rubix\ML\CrossValidation\KFold;

$params = [
    [1, 3, 5, 10], [true, false], [new Euclidean(), new Manhattan()],

$estimator = new GridSearch(KNearestNeighbors::class, $params, new FBeta(), new KFold(5));

Additional Methods#

Return the base learner instance:

public base() : ?\Rubix\ML\Learner