class_docstr: RANSAC (RANdom SAmple Consensus) algorithm.
  
  RANSAC is an iterative algorithm for the robust estimation of parameters
  from a subset of inliers from the complete data set.
  
  Read more in the :ref:`User Guide <ransac_regression>`.
  
  Parameters
  ----------
  estimator : object, default=None
      Base estimator object which implements the following methods:
  
       * `fit(X, y)`: Fit model to given training data and target values.
       * `score(X, y)`: Returns the mean accuracy on the given test data,
         which is used for the stop criterion defined by `stop_score`.
         Additionally, the score is used to decide which of two equally
         large consensus sets is chosen as the better one.
       * `predict(X)`: Returns predicted values using the linear model,
         which is used to compute residual error using loss function.
  
      If `estimator` is None, then
      :class:`~sklearn.linear_model.LinearRegression` is used for
      target values of dtype float.
  
      Note that the current implementation only supports regression
      estimators.
  
  min_samples : int (>= 1) or float ([0, 1]), default=None
      Minimum number of samples chosen randomly from original data. Treated
      as an absolute number of samples for `min_samples >= 1`, treated as a
      relative number `ceil(min_samples * X.shape[0])` for
      `min_samples < 1`. This is typically chosen as the minimal number of
      samples necessary to estimate the given `estimator`. By default a
      :class:`~sklearn.linear_model.LinearRegression` estimator is assumed and
      `min_samples` is chosen as ``X.shape[1] + 1``. This parameter is highly
      dependent upon the model, so if a `estimator` other than
      :class:`~sklearn.linear_model.LinearRegression` is used, the user must
      provide a value.
  
  residual_threshold : float, default=None
      Maximum residual for a data sample to be classified as an inlier.
      By default the threshold is chosen as the MAD (median absolute
      deviation) of the target values `y`. Points whose residuals are
      strictly equal to the threshold are considered as inliers.
  
  is_data_valid : callable, default=None
      This function is called with the randomly selected data before the
      model is fitted to it: `is_data_valid(X, y)`. If its return value is
      False the current randomly chosen sub-sample is skipped.
  
  is_model_valid : callable, default=None
      This function is called with the estimated model and the randomly
      selected data: `is_model_valid(model, X, y)`. If its return value is
      False the current randomly chosen sub-sample is skipped.
      Rejecting samples with this function is computationally costlier than
      with `is_data_valid`. `is_model_valid` should therefore only be used if
      the estimated model is needed for making the rejection decision.
  
  max_trials : int, default=100
      Maximum number of iterations for random sample selection.
  
  max_skips : int, default=np.inf
      Maximum number of iterations that can be skipped due to finding zero
      inliers or invalid data defined by ``is_data_valid`` or invalid models
      defined by ``is_model_valid``.
  
      .. versionadded:: 0.19
  
  stop_n_inliers : int, default=np.inf
      Stop iteration if at least this number of inliers are found.
  
  stop_score : float, default=np.inf
      Stop iteration if score is greater equal than this threshold.
  
  stop_probability : float in range [0, 1], default=0.99
      RANSAC iteration stops if at least one outlier-free set of the training
      data is sampled in RANSAC. This requires to generate at least N
      samples (iterations)::
  
          N >= log(1 - probability) / log(1 - e**m)
  
      where the probability (confidence) is typically set to high value such
      as 0.99 (the default) and e is the current fraction of inliers w.r.t.
      the total number of samples.
  
  loss : str, callable, default='absolute_error'
      String inputs, 'absolute_error' and 'squared_error' are supported which
      find the absolute error and squared error per sample respectively.
  
      If ``loss`` is a callable, then it should be a function that takes
      two arrays as inputs, the true and predicted value and returns a 1-D
      array with the i-th value of the array corresponding to the loss
      on ``X[i]``.
  
      If the loss on a sample is greater than the ``residual_threshold``,
      then this sample is classified as an outlier.
  
      .. versionadded:: 0.18
  
  random_state : int, RandomState instance, default=None
      The generator used to initialize the centers.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  Attributes
  ----------
  estimator_ : object
      Best fitted model (copy of the `estimator` object).
  
  n_trials_ : int
      Number of random selection trials until one of the stop criteria is
      met. It is always ``<= max_trials``.
  
  inlier_mask_ : bool array of shape [n_samples]
      Boolean mask of inliers classified as ``True``.
  
  n_skips_no_inliers_ : int
      Number of iterations skipped due to finding zero inliers.
  
      .. versionadded:: 0.19
  
  n_skips_invalid_data_ : int
      Number of iterations skipped due to invalid data defined by
      ``is_data_valid``.
  
      .. versionadded:: 0.19
  
  n_skips_invalid_model_ : int
      Number of iterations skipped due to an invalid model defined by
      ``is_model_valid``.
  
      .. versionadded:: 0.19
  
  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
  
  See Also
  --------
  HuberRegressor : Linear regression model that is robust to outliers.
  TheilSenRegressor : Theil-Sen Estimator robust multivariate regression model.
  SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD.
  
  References
  ----------
  .. [1] https://en.wikipedia.org/wiki/RANSAC
  .. [2] https://www.sri.com/wp-content/uploads/2021/12/ransac-publication.pdf
  .. [3] http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf
  
  Examples
  --------
  >>> from sklearn.linear_model import RANSACRegressor
  >>> from sklearn.datasets import make_regression
  >>> X, y = make_regression(
  ...     n_samples=200, n_features=2, noise=4.0, random_state=0)
  >>> reg = RANSACRegressor(random_state=0).fit(X, y)
  >>> reg.score(X, y)
  0.9885...
  >>> reg.predict(X[:1,])
  array([-31.9417...])
docstr: Predict using the estimated model.
  
  This is a wrapper for `estimator_.predict(X)`.
  
  Parameters
  ----------
  X : {array-like or sparse matrix} of shape (n_samples, n_features)
      Input data.
  
  Returns
  -------
  y : array, shape = [n_samples] or [n_samples, n_targets]
      Returns predicted values.
docstr_tok: ["Predict","using","the","estimated","model",".","This","is","a","wrapper","for","`","estimator_.predict","(","X",")","`",".","Parameters","--","--","--","--","--","X",":","{","array-like","or","sparse","matrix","}","of","shape","(","n_samples",",","n_features",")","Input","data",".","Returns","--","--","--","-","y",":","array",",","shape","=","[","n_samples","]","or","[","n_samples",",","n_targets","]","Returns","predicted","values","."]
code: def predict(self, X):
      """Predict using the estimated model.
  
      This is a wrapper for `estimator_.predict(X)`.
  
      Parameters
      ----------
      X : {array-like or sparse matrix} of shape (n_samples, n_features)
          Input data.
  
      Returns
      -------
      y : array, shape = [n_samples] or [n_samples, n_targets]
          Returns predicted values.
      """
      check_is_fitted(self)
      X = self._validate_data(
          X,
          force_all_finite=False,
          accept_sparse=True,
          reset=False,
      )
      return self.estimator_.predict(X)
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, MetaEstimatorMixin, MultiOutputMixin, RegressorMixin, _fit_context, clone
  from ..exceptions import ConvergenceWarning
  from ..utils import check_consistent_length, check_random_state
  from ..utils._param_validation import HasMethods, Interval, Options, RealNotInt, StrOptions
  from ..utils.metadata_routing import _raise_for_unsupported_routing, _RoutingNotSupportedMixin
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_sample_weight, check_is_fitted, has_fit_parameter
  from ._base import LinearRegression
label_desc: Use image node_id 3 for calling the RANSACRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: self