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DOC Add quantile loss to user guide on HGBT regression #29063

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Jul 23, 2024
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DOC Add quantile loss to user guide on HGBT regression #29063

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DOC Add quantile loss to user guide on HGBT regression
May 21, 2024
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Update doc/modules/ensemble.rst
ArturoAmorQ May 21, 2024
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ArturoAmorQ Jun 11, 2024
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23 changes: 15 additions & 8 deletions doc/modules/ensemble.rst
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Expand Up @@ -98,14 +98,21 @@ controls the number of iterations of the boosting process::
>>> clf.score(X_test, y_test)
0.8965

Available losses for regression are 'squared_error',
'absolute_error', which is less sensitive to outliers, and
'poisson', which is well suited to model counts and frequencies. For
classification, 'log_loss' is the only option. For binary classification it uses the
binary log loss, also known as binomial deviance or binary cross-entropy. For
`n_classes >= 3`, it uses the multi-class log loss function, with multinomial deviance
and categorical cross-entropy as alternative names. The appropriate loss version is
selected based on :term:`y` passed to :term:`fit`.
Available losses for **regression** are:

- 'squared_error', which is the default loss;
- 'absolute_error', which is less sensitive to outliers than the squared error;
- 'gamma', which is well suited to model strictly positive outcomes;
- 'poisson', which is well suited to model counts and frequencies;
- 'quantile', which allows for estimating a conditional quantile that can later
be used to obtain prediction intervals.

For **classification**, 'log_loss' is the only option. For binary classification
it uses the binary log loss, also known as binomial deviance or binary
cross-entropy. For `n_classes >= 3`, it uses the multi-class log loss function,
with multinomial deviance and categorical cross-entropy as alternative names.
The appropriate loss version is selected based on :term:`y` passed to
:term:`fit`.

The size of the trees can be controlled through the ``max_leaf_nodes``,
``max_depth``, and ``min_samples_leaf`` parameters.
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