`sklearn.linear_model`.Lars¶

class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute=’auto’, n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)[source]¶

Least Angle Regression model a.k.a. LAR

See also

lars_path, LarsCV
sklearn.decomposition.sparse_encode

Examples

>>> from sklearn import linear_model
>>> reg = linear_model.Lars(n_nonzero_coefs=1)
>>> reg.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
... 
Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True,
   n_nonzero_coefs=1, normalize=True, positive=False, precompute='auto',
   verbose=False)
>>> print(reg.coef_) 
[ 0. -1.11...]

Methods

`fit`(self, X, y[, Xy])	Fit the model using X, y as 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, fit_intercept=True, verbose=False, normalize=True, precompute=’auto’, n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)[source]¶

fit(self, X, y, Xy=None)[source]¶

Fit the model using X, y as training data.

Parameters:	X : array-like, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Target values. Xy : array-like, shape (n_samples,) or (n_samples, n_targets), optional Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
Returns:	self : object returns an instance of self.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

predict(self, X)[source]¶

Predict using the linear model

Parameters:	X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns:	C : array, 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:

X : array-like, 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.
y : array-like, shape = (n_samples) or (n_samples, n_outputs): True values for X.
sample_weight : array-like, shape = [n_samples], optional: Sample weights.

Returns:

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

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with metrics.r2_score. This will influence the score method of all the multioutput regressors (except for multioutput.MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call metrics.r2_score directly or make a custom scorer with metrics.make_scorer (the built-in scorer 'r2' uses multioutput='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

sklearn.linear_model.Lars¶

`sklearn.linear_model`.Lars¶