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doc: local trees parameter documentation #2636

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19051b9
doc: local trees parameter documentation
ethanglaser Jul 30, 2025
1242c79
Update doc/sources/algorithms.rst
ethanglaser Jul 30, 2025
f279089
Update doc/sources/algorithms.rst
ethanglaser Jul 30, 2025
6f0363a
Merge branch 'main' into dev/eglaser-local-trees-doc
ethanglaser Sep 26, 2025
1d075e5
add local_trees_mode details to tuning guide
ethanglaser Sep 26, 2025
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8 changes: 8 additions & 0 deletions doc/sources/algorithms.rst
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Expand Up @@ -487,6 +487,10 @@ Classification
- ``criterion`` != `'gini'`
- ``oob_score`` = `True`
- ``sample_weight`` != `None`

**Additional parameters:**

- ``local_trees_mode`` (bool, default=False): Enables local trees mode for distributed training. ``n_estimators`` is per rank, with isolated learning occurring on each processor before merging into a single model. This mode is experimental but scales better than default. This parameter is specific to the SPMD implementation and is not present in the standard scikit-learn API.
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I'd say this is not very descriptive.

  • Does it mean that the result has n_estimators*n_ranks trees?
  • Does the data get moved across ranks, or does each rank use the data that it owns?
  • Maybe could also refer to them as 'rank/nodes' as otherwise it might not be immediately clear what a 'rank' here refers to.

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Ideally we could point to oneDAL docs, where this functionality was implemented. @Alexandr-Solovev can we get this documented in oneDAL?

- Multi-output and sparse data are not supported
* - :obj:`sklearn.ensemble.ExtraTreesClassifier`
- All parameters are supported except:
Expand Down Expand Up @@ -539,6 +543,10 @@ Regression
- ``criterion`` != `'mse'`
- ``oob_score`` = `True`
- ``sample_weight`` != `None`

**Additional parameters:**

- ``local_trees_mode`` (bool, default=False): Enables local trees mode for distributed training. ``n_estimators`` is per rank, with isolated learning occurring on each processor before merging into a single model. This mode is experimental but scales better than default. This parameter is specific to the SPMD implementation and is not present in the standard scikit-learn API.
- Multi-output and sparse data are not supported
* - :obj:`sklearn.ensemble.ExtraTreesRegressor`
- All parameters are supported except:
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7 changes: 7 additions & 0 deletions doc/sources/guide/acceleration.rst
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Expand Up @@ -73,3 +73,10 @@ times, especially for larger data sets. However, due to the reduced fidelity of
the data, the resulting model can present worse performance metrics compared to
a model trained on the original data. In such cases, the number of bins can be
increased with the ``max_bins`` parameter.

Another parameter that can improve performance at large scale for Random Forest,
specifically the ``sklearnex.spmd.ensemble`` ``RandomForestClassifier`` and
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Could use links to the sklearn docs of the classes here, as done elsewhere - e.g. :obj:`sklearn.ensemble.RandomForestClassifier`

``RandomForestRegressor`` classes, is ``local_trees_mode``. This uses an
alternative backend that is more conducive to scalability when running on more
GPUs. The default is ``False``, but setting to ``True`` enables this functionality.
This parameter is only available in the ``spmd`` module, for multi-GPU use.
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