1# Online 2Learners 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. 3 4## Partially Train 5To partially train an Online learner pass it a training set to its `partial()` method: 6```php 7public partial(Dataset $dataset) : void 8``` 9 10```php 11$folds = $dataset->fold(3); 12 13$estimator->partial($folds[0]); 14 15$estimator->partial($folds[1]); 16 17$estimator->partial($folds[2]); 18``` 19 20!!! note 21 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.