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.
To partially train an Online learner pass it a training set to its
public partial(Dataset $dataset) : void
$folds = $dataset->fold(3); $estimator->partial($folds); $estimator->partial($folds); $estimator->partial($folds);
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.