val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?epsilon:float ->
?max_iter:int ->
?alpha:float ->
?warm_start:bool ->
?fit_intercept:bool ->
?tol:float ->
unit ->
tLinear regression model that is robust to outliers.
The Huber Regressor optimizes the squared loss for the samples where ``|(y - X'w) / sigma| < epsilon`` and the absolute loss for the samples where ``|(y - X'w) / sigma| > epsilon``, where w and sigma are parameters to be optimized. The parameter sigma makes sure that if y is scaled up or down by a certain factor, one does not need to rescale epsilon to achieve the same robustness. Note that this does not take into account the fact that the different features of X may be of different scales.
This makes sure that the loss function is not heavily influenced by the outliers while not completely ignoring their effect.
Read more in the :ref:`User Guide <huber_regression>`
.. versionadded:: 0.18
Parameters ---------- epsilon : float, greater than 1.0, default 1.35 The parameter epsilon controls the number of samples that should be classified as outliers. The smaller the epsilon, the more robust it is to outliers.
max_iter : int, default 100 Maximum number of iterations that ``scipy.optimize.minimize(method="L-BFGS-B")`` should run for.
alpha : float, default 0.0001 Regularization parameter.
warm_start : bool, default False This is useful if the stored attributes of a previously used model has to be reused. If set to False, then the coefficients will be rewritten for every call to fit. See :term:`the Glossary <warm_start>`.
fit_intercept : bool, default True Whether or not to fit the intercept. This can be set to False if the data is already centered around the origin.
tol : float, default 1e-5 The iteration will stop when ``max |proj g_i | i = 1, ..., n`` <= ``tol`` where pg_i is the i-th component of the projected gradient.
Attributes ---------- coef_ : array, shape (n_features,) Features got by optimizing the Huber loss.
intercept_ : float Bias.
scale_ : float The value by which ``|y - X'w - c|`` is scaled down.
n_iter_ : int Number of iterations that ``scipy.optimize.minimize(method="L-BFGS-B")`` has run for.
.. versionchanged:: 0.20
In SciPy <= 1.0.0 the number of lbfgs iterations may exceed ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
outliers_ : array, shape (n_samples,) A boolean mask which is set to True where the samples are identified as outliers.
Examples -------- >>> import numpy as np >>> from sklearn.linear_model import HuberRegressor, LinearRegression >>> from sklearn.datasets import make_regression >>> rng = np.random.RandomState(0) >>> X, y, coef = make_regression( ... n_samples=200, n_features=2, noise=4.0, coef=True, random_state=0) >>> X:4 = rng.uniform(10, 20, (4, 2)) >>> y:4 = rng.uniform(10, 20, 4) >>> huber = HuberRegressor().fit(X, y) >>> huber.score(X, y) -7.284608623514573 >>> huber.predict(X:1,) array(806.7200...) >>> linear = LinearRegression().fit(X, y) >>> print("True coefficients:", coef) True coefficients: 20.4923... 34.1698... >>> print("Huber coefficients:", huber.coef_) Huber coefficients: 17.7906... 31.0106... >>> print("Linear Regression coefficients:", linear.coef_) Linear Regression coefficients: -1.9221... 7.0226...
References ---------- .. 1 Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics Concomitant scale estimates, pg 172 .. 2 Art B. Owen (2006), A robust hybrid of lasso and ridge regression. https://statweb.stanford.edu/~owen/reports/hhu.pdf
Fit the model according to the given training data.
Parameters ---------- X : array-like, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array-like, shape (n_samples,) Target vector relative to X.
sample_weight : array-like, shape (n_samples,) Weight given to each sample.
Returns ------- self : object
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
val predict :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict using the linear model.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- C : array, shape (n_samples,) Returns predicted values.
Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float R^2 of self.predict(X) wrt. y.
Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val scale_ : t -> floatAttribute scale_: see constructor for documentation
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.