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
    Final model fitted on the inliers predicted by the "best" model found
    during RANSAC sampling (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] https://bmva-archive.org.uk/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...])

For a more detailed example, see
:ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py`
)