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);
$estimator->train($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.