class_docstr: A decision tree regressor.
  
  Read more in the :ref:`User Guide <tree>`.
  
  Parameters
  ----------
  criterion : {"squared_error", "friedman_mse", "absolute_error",             "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.
  
      .. versionadded:: 0.18
         Mean Absolute Error (MAE) criterion.
  
      .. versionadded:: 0.24
          Poisson deviance criterion.
  
  splitter : {"best", "random"}, default="best"
      The strategy used to choose the split at each node. Supported
      strategies are "best" to choose the best split and "random" to choose
      the best random split.
  
  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.
  
  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.
  
  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.
  
  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.
  
  max_features : int, float or {"auto", "sqrt", "log2"}, default=None
      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, then `max_features=n_features`.
  
      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.
  
  random_state : int, RandomState instance or None, default=None
      Controls the randomness of the estimator. The features are always
      randomly permuted at each split, even if ``splitter`` is set to
      ``"best"``. When ``max_features < n_features``, the algorithm will
      select ``max_features`` at random at each split before finding the best
      split among them. But the best found split may vary across different
      runs, even if ``max_features=n_features``. That is the case, if the
      improvement of the criterion is identical for several splits and one
      split has to be selected at random. To obtain a deterministic behaviour
      during fitting, ``random_state`` has to be fixed to an integer.
      See :term:`Glossary <random_state>` for details.
  
  max_leaf_nodes : int, default=None
      Grow a tree 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.
  
  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
  
  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.
  
      .. versionadded:: 0.22
  
  Attributes
  ----------
  feature_importances_ : ndarray of shape (n_features,)
      The 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 [4]_.
  
      Warning: impurity-based feature importances can be misleading for
      high cardinality features (many unique values). See
      :func:`sklearn.inspection.permutation_importance` as an alternative.
  
  max_features_ : int
      The inferred value of max_features.
  
  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
  
  n_outputs_ : int
      The number of outputs when ``fit`` is performed.
  
  tree_ : Tree instance
      The underlying Tree object. Please refer to
      ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
      :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
      for basic usage of these attributes.
  
  See Also
  --------
  DecisionTreeClassifier : A decision tree classifier.
  
  Notes
  -----
  The default values for the parameters controlling the size of the trees
  (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
  unpruned trees which can potentially be very large on some data sets. To
  reduce memory consumption, the complexity and size of the trees should be
  controlled by setting those parameter values.
  
  References
  ----------
  
  .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
  
  .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
         and Regression Trees", Wadsworth, Belmont, CA, 1984.
  
  .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
         Learning", Springer, 2009.
  
  .. [4] L. Breiman, and A. Cutler, "Random Forests",
         https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
  
  Examples
  --------
  >>> from sklearn.datasets import load_diabetes
  >>> from sklearn.model_selection import cross_val_score
  >>> from sklearn.tree import DecisionTreeRegressor
  >>> X, y = load_diabetes(return_X_y=True)
  >>> regressor = DecisionTreeRegressor(random_state=0)
  >>> cross_val_score(regressor, X, y, cv=10)
  ...                    # doctest: +SKIP
  ...
  array([-0.39..., -0.46...,  0.02...,  0.06..., -0.50...,
         0.16...,  0.11..., -0.73..., -0.30..., -0.00...])
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docstr: Build a decision tree regressor from the training set (X, y).
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The training input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csc_matrix``.
  
  y : array-like of shape (n_samples,) or (n_samples, n_outputs)
      The target values (real numbers). Use ``dtype=np.float64`` and
      ``order='C'`` for maximum efficiency.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights. If None, then samples are equally weighted. Splits
      that would create child nodes with net zero or negative weight are
      ignored while searching for a split in each node.
  
  check_input : bool, default=True
      Allow to bypass several input checking.
      Don't use this parameter unless you know what you're doing.
  
  Returns
  -------
  self : DecisionTreeRegressor
      Fitted estimator.
code: def fit(self, X, y, sample_weight=None, check_input=True):
      """Build a decision tree regressor from the training set (X, y).
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The training input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csc_matrix``.
  
      y : array-like of shape (n_samples,) or (n_samples, n_outputs)
          The target values (real numbers). Use ``dtype=np.float64`` and
          ``order='C'`` for maximum efficiency.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights. If None, then samples are equally weighted. Splits
          that would create child nodes with net zero or negative weight are
          ignored while searching for a split in each node.
  
      check_input : bool, default=True
          Allow to bypass several input checking.
          Don't use this parameter unless you know what you're doing.
  
      Returns
      -------
      self : DecisionTreeRegressor
          Fitted estimator.
      """
  
      super()._fit(
          X,
          y,
          sample_weight=sample_weight,
          check_input=check_input,
      )
      return self
  
imports: import copy
  import numbers
  import warnings
  from abc import ABCMeta, abstractmethod
  from math import ceil
  from numbers import Integral, Real
  import numpy
  from scipy.sparse import issparse
  from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context, clone, is_classifier
  from ..utils import Bunch, check_random_state, compute_sample_weight
  from ..utils._param_validation import Hidden, Interval, RealNotInt, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _assert_all_finite_element_wise, _check_sample_weight, assert_all_finite, check_is_fitted
  from .None import _criterion, _splitter, _tree
  from ._criterion import Criterion
  from ._splitter import Splitter
  from ._tree import BestFirstTreeBuilder, DepthFirstTreeBuilder, Tree, _build_pruned_tree_ccp, ccp_pruning_path
  from ._utils import _any_isnan_axis0
label_desc: Use image node_id 2 for calling the DecisionTreeRegressor obj's underlying member method code with example usage: obj.fit(X, y, sample_weight, check_input) and returns: self