class lightning.regression.CDRegressor(C=1.0, alpha=1.0, loss='squared', penalty='l2', max_iter=50, tol=0.001, termination='violation_sum', shrinking=True, max_steps=30, sigma=0.01, beta=0.5, warm_start=False, debiasing=False, Cd=1.0, warm_debiasing=False, selection='cyclic', permute=True, callback=None, n_calls=100, random_state=None, verbose=0, n_jobs=1)[source]

Estimator for learning linear regressors by (block) coordinate descent.

The objective functions considered take the form

minimize F(W) = C * L(W) + alpha * R(W),

where L(W) is a loss term and R(W) is a penalty term.

  • loss (str, 'squared') – The loss function to be used.

  • penalty (str, 'l2', 'l1', 'l1/l2') –

    The penalty to be used.

    • l2: ridge

    • l1: lasso

    • l1/l2: group lasso

  • other parameters (For) –

  • CDClassifier. (see) –

fit(X, y)[source]

Fit model according to X and y.

  • 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] or [n_samples, n_targets]) – Target values.


self – Returns self.

Return type



Get parameters for this estimator.


deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.


params – Parameter names mapped to their values.

Return type


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.

  • 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.


score\(R^2\) of self.predict(X) wrt. y.

Return type



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 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.


**params (dict) – Estimator parameters.


self – Estimator instance.

Return type

estimator instance