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Feb 22, 2023
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DOC improve the warm_start glossary entry #25523

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DOC improve the glossary entry
glemaitre Jan 31, 2023
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glemaitre Feb 2, 2023
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DOC be more specific regarding ohther models
glemaitre Feb 2, 2023
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glemaitre Feb 20, 2023
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22 changes: 18 additions & 4 deletions doc/glossary.rst
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Expand Up @@ -1666,10 +1666,24 @@ functions or non-estimator constructors.
in a subsequent call to :term:`fit`.

Note that this is only applicable for some models and some
parameters, and even some orders of parameter values. For example,
``warm_start`` may be used when building random forests to add more
trees to the forest (increasing ``n_estimators``) but not to reduce
their number.
parameters, and even some orders of parameter values. In general, there
is an interaction between ``warm_start`` and the parameter controlling
the number of iterations of the estimator.

For estimators imported from :mod:`ensemble`,
``warm_start`` will interact with ``n_estimators`` or ``max_iter``.
For these models, the number of iterations, reported via
``len(estimators_)`` or ``n_iter_``, corresponds the total number of
estimators/iterations learnt since the initialization of the model.
Thus, if a model was already initialized with `N`` estimators, and `fit`
is called with ``n_estimators`` or ``max_iter`` set to `M`, the model
will train `M - N` new estimators.

Other models, usually using gradient-based solvers, have a different
behavior. They all expose a ``max_iter`` parameter. The reported
``n_iter_`` corresponds to the number of iteration done during the last
call to ``fit`` and will be at most ``max_iter``. Thus, we do not
consider the state of the estimator since the initialization.

:term:`partial_fit` also retains the model between calls, but differs:
with ``warm_start`` the parameters change and the data is
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