sklearn.linear_model.Lars¶
- class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True)¶
Least Angle Regression model a.k.a. LAR
Parameters : n_nonzero_coefs : int, optional
Target number of non-zero coefficients. Use np.inf for no limit.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
precompute : True | False | ‘auto’ | array-like
Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
eps: float, optional :
The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the tol parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.
fit_path : boolean
If True the full path is stored in the coef_path_ attribute. If you compute the solution for a large problem or many targets, setting fit_path to False will lead to a speedup, especially with a small alpha.
See also
Examples
>>> from sklearn import linear_model >>> clf = linear_model.Lars(n_nonzero_coefs=1) >>> clf.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, precompute='auto', verbose=False) >>> print(clf.coef_) [ 0. -1.11...]
Attributes
alphas_ array, shape (n_alphas + 1,) | list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. n_alphas is either n_nonzero_coefs or n_features, whichever is smaller. active_ list, length = n_alphas | list of n_targets such lists Indices of active variables at the end of the path. coef_path_ array, shape (n_features, n_alphas + 1) | list of n_targets such arrays The varying values of the coefficients along the path. It is not present if the fit_path parameter is False. coef_ array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula). intercept_ float | array, shape (n_targets,) Independent term in decision function. Methods
decision_function(X) Decision function of the linear model. fit(X, y[, Xy]) Fit the model using X, y as training data. get_params([deep]) Get parameters for this estimator. predict(X) Predict using the linear model score(X, y) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of this estimator. - __init__(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True)¶
- decision_function(X)¶
Decision function of the linear model.
Parameters : X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns : C : array, shape = (n_samples,)
Returns predicted values.
- fit(X, y, Xy=None)¶
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(deep=True)¶
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(X)¶
Predict using the linear model
Parameters : X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns : C : array, shape = (n_samples,)
Returns predicted values.
- score(X, y)¶
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.
Parameters : X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,)
True values for X.
Returns : score : float
R^2 of self.predict(X) wrt. y.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :