LinearSVR#

classsklearn.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 ofliblinear rather than libsvm, so it has more flexibility in the choice ofpenalties and loss functions and should scale better to large numbers ofsamples.

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

This class supports both dense and sparse input.

Read more in theUser Guide.

Added in version 0.16.

Parameters:

epsilonfloat, default=0.0

Epsilon parameter in the epsilon-insensitive loss function. Notethat the value of this parameter depends on the scale of the targetvariable y. If unsure, setepsilon=0.

tolfloat, default=1e-4

Tolerance for stopping criteria.

Cfloat, default=1.0

Regularization parameter. The strength of the regularization isinversely 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-insensitiveloss (‘squared_epsilon_insensitive’) is the L2 loss.

fit_interceptbool, default=True

Whether or not to fit an intercept. If set to True, the feature vectoris extended to include an intercept term:[x_1,...,x_n,1], where1 corresponds to the intercept. If set to False, no intercept will beused in calculations (i.e. data is expected to be already centered).

intercept_scalingfloat, default=1.0

Whenfit_intercept is True, the instance vector x becomes[x_1,...,x_n,intercept_scaling], i.e. a “synthetic” feature with a constantvalue equal tointercept_scaling is appended to the instance vector.The intercept becomes intercept_scaling * synthetic feature weight.Note that liblinear internally penalizes the intercept, treating itlike any other term in the feature vector. To reduce the impact of theregularization on the intercept, theintercept_scaling parameter canbe set to a value greater than 1; the higher the value ofintercept_scaling, the lower the impact of regularization on it.Then, the weights become[w_x_1,...,w_x_n,w_intercept*intercept_scaling], wherew_x_1,...,w_x_n representthe feature weights and the intercept weight is scaled byintercept_scaling. This scaling allows the intercept term to have adifferent regularization behavior compared to the other features.

dual“auto” or bool, default=”auto”

Select the algorithm to either solve the dual or primaloptimization problem. Prefer dual=False when n_samples > n_features.dual="auto" will choose the value of the parameter automatically,based on the values ofn_samples,n_features andloss. Ifn_samples <n_features and optimizer supports chosenloss,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 defaultin version 1.5.

verboseint, default=0

Enable verbose output. Note that this setting takes advantage of aper-process runtime setting in liblinear that, if enabled, may not workproperly 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.SeeGlossary.

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 primalproblem).

coef_ is a readonly property derived fromraw_coef_ thatfollows 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 duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas 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 asLinearSVR 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

>>>fromsklearn.svmimportLinearSVR>>>fromsklearn.pipelineimportmake_pipeline>>>fromsklearn.preprocessingimportStandardScaler>>>fromsklearn.datasetsimportmake_regression>>>X,y=make_regression(n_features=4,random_state=0)>>>regr=make_pipeline(StandardScaler(),...LinearSVR(random_state=0,tol=1e-5))>>>regr.fit(X,y)Pipeline(steps=[('standardscaler', StandardScaler()),                ('linearsvr', LinearSVR(random_state=0, tol=1e-05))])

>>>print(regr.named_steps['linearsvr'].coef_)[18.582 27.023 44.357 64.522]>>>print(regr.named_steps['linearsvr'].intercept_)[-4.]>>>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, wheren_samples is the number of samples andn_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 individualsamples. 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 checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained 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]#

Returncoefficient of determination on test data.

The coefficient of determination,\(R^2\), is defined as\((1 - \frac{u}{v})\), where\(u\) is the residualsum 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 themodel can be arbitrarily worse). A constant model that always predictsthe expected value ofy, disregarding the input features, would geta\(R^2\) score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputedkernel matrix or a list of generic objects instead with shape(n_samples,n_samples_fitted), wheren_samples_fittedis the number of samples used in the fitting for the estimator.

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

True values forX.

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

Sample weights.

Returns:

scorefloat

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

Notes

The\(R^2\) score used when callingscore on a regressor usesmultioutput='uniform_average' from version 0.23 to keep consistentwith default value ofr2_score.This influences thescore method of all the multioutputregressors (except forMultiOutputRegressor).

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

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

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

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

• 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter infit.

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 asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible 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]#

Configure whether metadata should be requested to be passed to thescore method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

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

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

• 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter inscore.

Returns:

selfobject

The updated object.