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doc update (#490)

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Chi Wang 2022-03-21 17:39:03 +00:00
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commit 1d029436e7
3 changed files with 4 additions and 3 deletions
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flaml/version.py
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@ -1 +1 @@
__version__ = "0.10.0" __version__ = "0.10.1"

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website/docs/Use-Cases/Task-Oriented-AutoML.md
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@ -301,7 +301,8 @@ By default, flaml uses the following method to split the data:
* time-based split for time series forecasting; * time-based split for time series forecasting;
* group-based split for learning to rank. * group-based split for learning to rank.
The data split method for classification can be changed into uniform split by setting `split_type="uniform"`. For both classification and regression, time-based split can be enforced if the data are sorted by timestamps, by setting `split_type="time"`. 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 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
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold) [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)

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website/docs/Use-Cases/Zero-Shot-AutoML.md
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@ -19,7 +19,7 @@ from lightgbm import LGBMRegressor
estimator = LGBMRegressor() estimator = LGBMRegressor()
estimator.fit(X_train, y_train) estimator.fit(X_train, y_train)
estimator.predict(X_test, y_test) estimator.predict(X_test)
``` ```
Simply replace the first line with: Simply replace the first line with: