LinearSVR#

class sklearn.svm.LinearSVR(*, epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual='auto', verbose=0, random_state=None, max_iter=1000)[source]#

Linear Support Vector Regression.

Similar to SVR with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

The main differences between LinearSVR and SVR lie in the loss function used by default, and in the handling of intercept regularization between those two implementations.

This class supports both dense and sparse input.

Read more in the User guide.

Added in version 0.16.

Parameters:
epsilonfloat, default=0.0

Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set epsilon=0.

tolfloat, default=1e-4

Tolerance for stopping criteria.

Cfloat, default=1.0

Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.

loss{‘epsilon_insensitive’, ‘squared_epsilon_insensitive’}, default=’epsilon_insensitive’

Specifies the loss function. The epsilon-insensitive loss (standard SVR) is the L1 loss, while the squared epsilon-insensitive loss (‘squared_epsilon_insensitive’) is the L2 loss.

fit_interceptbool, default=True

Whether or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: [x_1, ..., x_n, 1], where 1 corresponds to the intercept. If set to False, no intercept will be used in calculations (i.e. data is expected to be already centered).

intercept_scalingfloat, default=1.0

When fit_intercept is True, the instance vector x becomes [x_1, ..., x_n, intercept_scaling], i.e. a “synthetic” feature with a constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight. Note that liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the intercept_scaling parameter can be set to a value greater than 1; the higher the value of intercept_scaling, the lower the impact of regularization on it. Then, the weights become [w_x_1, ..., w_x_n, w_intercept*intercept_scaling], where w_x_1, ..., w_x_n represent the feature weights and the intercept weight is scaled by intercept_scaling. This scaling allows the intercept term to have a different regularization behavior compared to the other features.

dual“auto” or bool, default=”auto”

Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples &gt; n_features. dual="auto" will choose the value of the parameter automatically, based on the values of n_samples, n_features and loss. If n_samples < n_features and optimizer supports chosen loss, then dual will be set to True, otherwise it will be set to False.

Changed in version 1.3: The "auto" option is added in version 1.3 and will be the default in version 1.5.

verboseint, default=0

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.

random_stateint, RandomState instance or None, default=None

Controls the pseudo random number generation for shuffling the data. Pass an int for reproducible output across multiple function calls. See glossary.

max_iterint, default=1000

The maximum number of iterations to be run.

Attributes:
coef_ndarray of shape (n_features) if n_classes == 2 else (n_classes, n_features)

Weights assigned to the features (coefficients in the primal problem).

coef_ is a readonly property derived from raw_coef_ that follows the internal memory layout of liblinear.

intercept_ndarray of shape (1) if n_classes == 2 else (n_classes)

Constants in decision function.

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

n_iter_int

Maximum number of iterations run across all classes.

See also

LinearSVC

Implementation of Support Vector Machine classifier using the same library as this class (liblinear).

SVR

Implementation of Support Vector Machine regression using libsvm: the kernel can be non-linear but its SMO algorithm does not scale to large number of samples as LinearSVR does.

sklearn.linear_model.SgDRegressor

SgDRegressor can optimize the same cost function as LinearSVR by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.

Examples

&gt;&gt;&gt; from sklearn.svm import LinearSVR
&gt;&gt;&gt; from sklearn.pipeline import make_pipeline
&gt;&gt;&gt; from sklearn.preprocessing import StandardScaler
&gt;&gt;&gt; from sklearn.datasets import make_regression
&gt;&gt;&gt; X, y = make_regression(n_features=4, random_state=0)
&gt;&gt;&gt; regr = make_pipeline(StandardScaler(),
...                      LinearSVR(random_state=0, tol=1e-5))
&gt;&gt;&gt; regr.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('linearsvr', LinearSVR(random_state=0, tol=1e-05))])

&gt;&gt;&gt; print(regr.named_steps['linearsvr'].coef_)
[18.582... 27.023... 44.357... 64.522...]
&gt;&gt;&gt; print(regr.named_steps['linearsvr'].intercept_)
[-4...]
&gt;&gt;&gt; print(regr.predict([[0, 0, 0, 0]]))
[-2.384...]
fit(X, y, sample_weight=None)[source]#

Fit the model according to the given training data.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target vector relative to X.

sample_weightarray-like of shape (n_samples,), default=None

Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Added in version 0.18.

Returns:
selfobject

An instance of the estimator.

get_metadata_routing()[source]#

get metadata routing of this object.

Please check User guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X)[source]#

Predict using the linear model.

Parameters:
Xarray-like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns:
Carray, shape (n_samples,)

Returns predicted values.

score(X, y, sample_weight=None)[source]#

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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:
Xarray-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 with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
scorefloat

\(R^2\) of self.predict(X) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → LinearSVR[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANgED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component&gt;__<parameter&gt; so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → LinearSVR[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANgED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

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