# lightning.regression.LinearSVR¶

class lightning.regression.LinearSVR(C=1.0, epsilon=0, loss='epsilon_insensitive', max_iter=1000, tol=0.001, fit_intercept=False, permute=True, warm_start=False, random_state=None, callback=None, n_calls=100, verbose=0)[source]

Estimator for learning a linear support vector regressor by coordinate descent in the dual.

Parameters
• loss (str, 'epsilon_insensitive', 'squared_epsilon_insensitive') – The loss function to be used.

• C (float) – Weight of the loss term.

• epsilon (float) – Parameter of the epsilon-insensitive loss.

• max_iter (int) – Maximum number of iterations to perform.

• tol (float) – Tolerance of the stopping criterion.

• fit_intercept (bool) – Whether to fit an intercept term or not.

• warm_start (bool) – Whether to activate warm-start or not.

• permute (bool) – Whether to permute coordinates or not before cycling.

• callback (callable) – Callback function.

• n_calls (int) – Frequency with which callback must be called.

• random_state (RandomState or int) – The seed of the pseudo random number generator to use.

• verbose (int) – Verbosity level.

fit(X, y)[source]

Fit model according to X and y.

Parameters
• X (array-like, shape = [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

• y (array-like, shape = [n_samples]) – Target values.

Returns

self – Returns self.

Return type

regressor

get_params(deep=True)

Get 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 – Parameter names mapped to their values.

Return type

dict

n_nonzero(percentage=False)
predict(X)
score(X, y, sample_weight=None)

Return the coefficient of determination $$R^2$$ of the prediction.

The coefficient $$R^2$$ 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
• 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 with 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$$R^2$$ of self.predict(X) wrt. y.

Return type

float

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_params(**params)

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>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance