\n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " n_estimators\n",
+ " n_estimators: int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.\n",
+ " \n",
+ " | \n",
+ " 100 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " criterion\n",
+ " criterion: {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"}, default=\"squared_error\"
The function to measure the quality of a split. Supported criteria
are \"squared_error\" for the mean squared error, which is equal to
variance reduction as feature selection criterion and minimizes the L2
loss using the mean of each terminal node, \"friedman_mse\", which uses
mean squared error with Friedman's improvement score for potential
splits, \"absolute_error\" for the mean absolute error, which minimizes
the L1 loss using the median of each terminal node, and \"poisson\" which
uses reduction in Poisson deviance to find splits.
Training using \"absolute_error\" is significantly slower
than when using \"squared_error\".
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
.. versionadded:: 1.0
Poisson criterion.\n",
+ " \n",
+ " | \n",
+ " 'squared_error' | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " max_depth\n",
+ " max_depth: int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.\n",
+ " \n",
+ " | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " min_samples_split\n",
+ " min_samples_split: int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.\n",
+ " \n",
+ " | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " min_samples_leaf\n",
+ " min_samples_leaf: int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.\n",
+ " \n",
+ " | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " min_weight_fraction_leaf\n",
+ " min_weight_fraction_leaf: float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.\n",
+ " \n",
+ " | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " max_features\n",
+ " max_features: {\"sqrt\", \"log2\", None}, int or float, default=1.0
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at each
split.
- If \"sqrt\", then `max_features=sqrt(n_features)`.
- If \"log2\", then `max_features=log2(n_features)`.
- If None or 1.0, then `max_features=n_features`.
.. note::
The default of 1.0 is equivalent to bagged trees and more
randomness can be achieved by setting smaller values, e.g. 0.3.
.. versionchanged:: 1.1
The default of `max_features` changed from `\"auto\"` to 1.0.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.\n",
+ " \n",
+ " | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " max_leaf_nodes\n",
+ " max_leaf_nodes: int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.\n",
+ " \n",
+ " | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " min_impurity_decrease\n",
+ " min_impurity_decrease: float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19\n",
+ " \n",
+ " | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " bootstrap\n",
+ " bootstrap: bool, default=True
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.\n",
+ " \n",
+ " | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " oob_score\n",
+ " oob_score: bool or callable, default=False
Whether to use out-of-bag samples to estimate the generalization score.
By default, :func:`~sklearn.metrics.r2_score` is used.
Provide a callable with signature `metric(y_true, y_pred)` to use a
custom metric. Only available if `bootstrap=True`.
For an illustration of out-of-bag (OOB) error estimation, see the example
:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.\n",
+ " \n",
+ " | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " n_jobs\n",
+ " n_jobs: int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
` for more details.\n",
+ " \n",
+ " | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " random_state\n",
+ " random_state: int, RandomState instance or None, default=None
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary ` for details.\n",
+ " \n",
+ " | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " verbose\n",
+ " verbose: int, default=0
Controls the verbosity when fitting and predicting.\n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " warm_start\n",
+ " warm_start: bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`Glossary ` and
:ref:`tree_ensemble_warm_start` for details.\n",
+ " \n",
+ " | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " ccp_alpha\n",
+ " ccp_alpha: non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details. See
:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
for an example of such pruning.
.. versionadded:: 0.22\n",
+ " \n",
+ " | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " max_samples\n",
+ " max_samples: int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
`max_samples` should be in the interval `(0.0, 1.0]`.
.. versionadded:: 0.22\n",
+ " \n",
+ " | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " monotonic_cst\n",
+ " monotonic_cst: array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonically increasing
- 0: no constraint
- -1: monotonically decreasing
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multioutput regressions (i.e. when `n_outputs_ > 1`),
- regressions trained on data with missing values.
Read more in the :ref:`User Guide `.
.. versionadded:: 1.4\n",
+ " \n",
+ " | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ " \n",
+ "