Online#

Learners that implement the Online interface can be trained in batches. Learners of this type are great for when you either have a continuous stream of data or a dataset that is too large to fit into memory. In addition, partial training allows the model to evolve over time.

Partially Train#

To partially train an Online learner pass it a training set to its partial() method:

public partial(Dataset $dataset) : void
$folds = $dataset->fold(3);

$estimator->partial($folds[0]);

$estimator->partial($folds[1]);

$estimator->partial($folds[2]);

Note: Learner will continue to train as long as you are using the partial() method, however, calling train() on a trained or partially trained learner will reset it back to baseline first.