se
    learners. If smaller than 1.0 this results in Stochastic Gradient
    Boosting. `subsample` interacts with the parameter `n_estimators`.
    Choosing `subsample < 1.0` leads to a reduction of variance
    and an increase in bias.
    Values must be in the range `(0.0, 1.0]`.

criterion : {'friedman_mse', 'squared_error'}, default='friedman_mse'
    The function to measure the quality of a split. Supported criteria are
    "friedman_mse" for the mean squared error with improvement score by
    Friedman, "squared_error" for mean squared error. The default value of
    "friedman_mse" is generally the best as it can provide a better
    approximation in some cases.

    .. versionadded:: 0.18

min_samples_split : int or float, default=2
    The minimum number of samples required to split an internal node:

    - If int, values must be in the range `[2, inf)`.
    - If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`
      will be `ceil(min_samples_split * n_samples)`.

    .. versionchanged:: 0.18
       Added float values for fractions.

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, values must be in the range `[1, inf)`.
    - If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`
      will be `ceil(min_samples_leaf * n_samples)`.

    .. versionchanged:: 0.18
       Added float values for fractions.

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.
    Values must be in the range `[0.0, 0.5]`.

max_depth : int or None, default=3
    Maximum depth of the individual regression estimators. The maximum
    depth limits the number of nodes in the tree. Tune this parameter
    for best performance; the best value depends on the interaction
    of the input variables. If None, then nodes are expanded until
    all leaves are pure or until all leaves contain less than
    min_samples_split samples.
    If int, values must be in the range `[1, inf)`.

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.
    Values must be in the range `[0.0, inf)`.

    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

init : estimator or 'zero', default=None
    An estimator object that is used to compute the initial predictions.
    ``init`` has to provide :term:`fit` and :term:`predict`. If 'zero', the
    initial raw predictions are set to zero. By default a
    ``DummyEstimator`` is used, predicting either the average target value
    (for loss='squared_error'), or a quantile for the other losses.

random_state : int, RandomState instance or None, default=None
    Controls the random seed given to each Tree estimator at each
    boosting iteration.
    In addition, it controls the random permutation of the features at
    each split (see Notes for more details).
    It also controls the random splitting of the training data to obtain a
    validation set if `n_iter_no_change` is not None.
    Pass an int for reproducible output across multiple function calls.
    See :term:`Glossary <random_state>`.

max_features : {'sqrt', 'log2'}, int or float, default=None
    The number of features to consider when looking for the best split:

    - If int, values must be in the range `[1, inf)`.
    - If float, values must be in the range `(0.0, 1.0]` and the features
      considered at each split will be `max(1, int(max_features * n_features_in_))`.
    - If "sqrt", then `max_features=sqrt(n_features)`.
    - If "log2", then `max_features=log2(n_features)`.
    - If None, then `max_features=n_features`.

    Choosing `max_features < n_features` leads to a reduction of variance
    and an increase in bias.

    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.

alpha : float, default=0.9
    The alpha-quantile of the huber loss function and the quantile
    loss function. Only if ``loss='huber'`` or ``loss='quantile'``.
    Values must be in the range `(0.0, 1.0)`.

verbose : int, default=0
    Enable verbose output. If 1 then it prints progress and performance
    once in a while (the more trees the lower the frequency). If greater
    than 1 then it prints progress and performance for every tree.
    Values must be in the range `[0, inf)`.

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.
    Values must be in the range `[2, inf)`.
    If None, then unlimited number of leaf nodes.

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 erase the
    previous solution. See :term:`the Glossary <warm_start>`.

validation_fraction : float, default=0.1
    The proportion of training data to set aside as validation set for
    early stopping. Values must be in the range `(0.0, 1.0)`.
    Only used if ``n_iter_no_change`` is set to an integer.

    .. versionadded:: 0.20

n_iter_no_change : int, default=None
    ``n_iter_no_change`` is used to decide if early stopping will be used
    to terminate training when validation score is not improving. By
    default it is set to None to disable early stopping. If set to a
    number, it will set aside ``validation_fraction`` size of the training
    data as validation and terminate training when validation score is not
    improving in all of the previous ``n_iter_no_change`` numbers of
    iterations.
    Values must be in the range `[1, inf)`.
    See
    :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.

    .. versionadded:: 0.20

tol : float, default=1e-4
    Tolerance for the early stopping. When the loss is not improving
    by at least tol for ``n_iter_no_change`` iterations (if set to a
    number), the training stops.
    Values must be in the range `[0.0, inf)`.

    .. versionadded:: 0.20

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.
    Values must be in the range `[0.0, inf)`.
    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

Attributes
----------
n_estimators_ : int
    The number of estimators as selected by early stopping (if
    ``n_iter_no_change`` is specified). Otherwise it is set to
    ``n_estimators``.

n_trees_per_iteration_ : int
    The number of trees that are built at each iteration. For regressors, this is
    always 1.

    .. versionadded:: 1.4.0

feature_importances_ : ndarray of shape (n_features,)
    The impurity-based feature importances.
    The higher, the more important the feature.
    The importance of a feature is computed as the (normalized)
    total reduction of the criterion brought by that feature.  It is also
    known as the Gini importance.

    Warning: impurity-based feature importances can be misleading for
    high cardinality features (many unique values). See
    :func:`sklearn.inspection.permutation_importance` as an alternative.

oob_improvement_ : ndarray of shape (n_estimators,)
    The improvement in loss on the out-of-bag samples
    relative to the previous iteration.
    ``oob_improvement_[0]`` is the improvement in
    loss of the first stage over the ``init`` estimator.
    Only available if ``subsample < 1.0``.

oob_scores_ : ndarray of shape (n_estimators,)
    The full history of the loss values on the out-of-bag
    samples. Only available if `subsample < 1.0`.

    .. versionadded:: 1.3

oob_score_ : float
    The last value of the loss on the out-of-bag samples. It is
    the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.

    .. versionadded:: 1.3

train_score_ : ndarray of shape (n_estimators,)
    The i-th score ``train_score_[i]`` is the loss of the
    model at iteration ``i`` on the in-bag sample.
    If ``subsample == 1`` this is the loss on the training data.

init_ : estimator
    The estimator that provides the initial predictions. Set via the ``init``
    argument.

estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, 1)
    The collection of fitted sub-estimators.

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

max_features_ : int
    The inferred value of max_features.

See Also
--------
HistGradientBoostingRegressor : Histogram-based Gradient Boosting
    Classification Tree.
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
sklearn.ensemble.RandomForestRegressor : A random forest regressor.

Notes
-----
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data and
``max_features=n_features``, if the improvement of the criterion is
identical for several splits enumerated during the search of the best
split. To obtain a deterministic behaviour during fitting,
``random_state`` has to be fixed.

References
----------
J. Friedman, Greedy Function Approximation: A Gradient Boosting
Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.

J. Friedman, Stochastic Gradient Boosting, 1999

T. Hastie, R. Tibshirani and J. Friedman.
Elements of Statistical Learning Ed. 2, Springer, 2009.

Examples
--------
>>> from sklearn.datasets import make_regression
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_regression(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> reg = GradientBoostingRegressor(random_state=0)
>>> reg.fit(X_train, y_train)
GradientBoostingRegressor(random_state=0)
>>> reg.predict(X_test[1:2])
array([-61...])
>>> reg.score(X_test, y_test)
0.4...

For a detailed example of utilizing
:class:`~sklearn.ensemble.GradientBoostingRegressor`
to fit an ensemble of weak predictive models, please refer to
:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`.
>