[source]

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: Wrapper, Estimator, Learner, Parallel, Persistable, Verbose

Data Type Compatibility: Depends on base learner

Parameters#

#	Param	Default	Type	Description
1	base		string	The class name of the base learner.
2	params		array	An array of n-tuples containing the possible values for each of the base learner's constructor parameters.
3	metric	null	Metric	The validation metric used to score each set of hyper-parameters. If null, automatically selects a default metric based on estimator type.
4	validator	KFold	Validator	The validator used to test and score each trained model.

Additional Methods#

Return an array containing the validation scores and hyper-parameters under test for each combination resulting from the last search:

public results() : ?array

Return an array containing the best parameters from the last search:

public best() : ?array

Example#

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));

$estimator->train($dataset);

var_dump($estimator->best());

array(3) {
  [0]=> int(3)
  [1]=> bool(true)
  [2]=> object(Rubix\ML\Kernels\Distance\Manhattan) {}
}