sklearn.svm
.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=True, 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.
This class supports both dense and sparse input.
Read more in the User Guide.
 Parameters
 epsilonfloat, optional (default=0.0)
Epsilon parameter in the epsiloninsensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set
epsilon=0
. tolfloat, optional (default=1e4)
Tolerance for stopping criteria.
 Cfloat, optional (default=1.0)
Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.
 lossstring, optional (default=’epsilon_insensitive’)
Specifies the loss function. The epsiloninsensitive loss (standard SVR) is the L1 loss, while the squared epsiloninsensitive loss (‘squared_epsilon_insensitive’) is the L2 loss.
 fit_interceptboolean, optional (default=True)
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).
 intercept_scalingfloat, optional (default=1)
When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
 dualbool, (default=True)
Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.
 verboseint, (default=0)
Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.
 random_stateint, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
. max_iterint, (default=1000)
The maximum number of iterations to be run.
 Attributes
 coef_array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_
is a readonly property derived fromraw_coef_
that follows the internal memory layout of liblinear. intercept_array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
 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 nonlinear but its SMO algorithm does not scale to large number of samples as LinearSVC 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
>>> from sklearn.svm import LinearSVR >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_features=4, random_state=0) >>> regr = LinearSVR(random_state=0, tol=1e5) >>> regr.fit(X, y) LinearSVR(random_state=0, tol=1e05) >>> print(regr.coef_) [16.35... 26.91... 42.30... 60.47...] >>> print(regr.intercept_) [4.29...] >>> print(regr.predict([[0, 0, 0, 0]])) [4.29...]
Methods
fit
(self, X, y[, sample_weight])Fit the model according to the given training data.
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X)Predict using the linear model
score
(self, X, y[, sample_weight])Returns the coefficient of determination R^2 of the prediction.
set_params
(self, \*\*params)Set the parameters of this estimator.

__init__
(self, epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y, sample_weight=None)[source]¶ Fit the model according to the given training data.
 Parameters
 X{arraylike, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
 yarraylike, shape = [n_samples]
Target vector relative to X
 sample_weightarraylike, shape = [n_samples], optional
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
 Returns
 selfobject

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, 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
(self, X, y, sample_weight=None)[source]¶ Returns 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
 Xarraylike, shape = (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike, shape = [n_samples], optional
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor will usemultioutput='uniform_average'
from version 0.23 to keep consistent withr2_score
. This will influence thescore
method of all the multioutput regressors (except forMultiOutputRegressor
). To specify the default value manually and avoid the warning, please either callr2_score
directly or make a custom scorer withmake_scorer
(the builtin scorer'r2'
usesmultioutput='uniform_average'
).

set_params
(self, **params)[source]¶ Set 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. Returns
 self