From c6f8e004e78cf1d728e898a0011284c1dfb4b41a Mon Sep 17 00:00:00 2001 From: skzhang1 Date: Mon, 30 Jan 2023 06:01:20 -0800 Subject: [PATCH] fix link --- website/docs/Use-Cases/Task-Oriented-AutoML.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md index eba19a631..5357c4881 100644 --- a/website/docs/Use-Cases/Task-Oriented-AutoML.md +++ b/website/docs/Use-Cases/Task-Oriented-AutoML.md @@ -375,7 +375,7 @@ The data split method for classification can be changed into uniform split by se 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. This is also shown in an [example notebook](https://github.com/microsoft/FLAML/blob/main/notebook/basics/understanding_cross_validation.ipynb.ipynb). +- group-based splits can be set by using `split_type="group"` while providing the group identifier for each sample through the `groups` argument. This is also shown in an [example notebook](https://github.com/microsoft/FLAML/blob/main/notebook/basics/understanding_cross_validation.ipynb). 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)