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.