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Andrea W 2023-01-26 14:31:54 +00:00
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website/docs/Use-Cases/Task-Oriented-AutoML.md
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@ -365,16 +365,19 @@ For cross validation, you can also set `n_splits` of the number of folds. By def
#### Data split method
By default, flaml uses the following method to split the data:
flaml relies on the provided task type to infer the default splitting strategy:
* stratified split for classification;
* uniform split for regression;
* time-based split for time series forecasting;
* group-based split for learning to rank.
The data split method for classification can be changed into uniform split by setting `split_type="uniform"`. The data are shuffled when `split_type in ("uniform", "stratified")`.
For both classification and regression, time-based split can be enforced if the data are sorted by timestamps, by setting `split_type="time"`.
When `eval_method="cv"`, `split_type` can also be set as a custom splitter. It needs to be an instance of a derived class of scikit-learn
For both classification and regression tasks more advanced split configurations are possible:
- time-based split can be enforced if the data are sorted by timestamps, by setting `split_type="time"`,
- group-based splits can be set by using `split_type="group"` while providing the group identifier for each sample through the `groups` argument.
More in general, `split_type` can also be set as a custom splitter object, when `eval_method="cv"`. It needs to be an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures. To disable shuffling, the splitter instance must contain the attribute `shuffle=False`.