8.15.1.10. sklearn.linear_model.LassoLars¶
- class sklearn.linear_model.LassoLars(alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, copy_X=True)¶
Lasso model fit with Least Angle Regression a.k.a. Lars
It is a Linear Model trained with an L1 prior as regularizer.
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Parameters : 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
If True, the regressors X are normalized
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
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.
max_iter: integer, optional :
Maximum number of iterations to perform.
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.
See also
lars_path, lasso_path, Lasso, LassoCV, LassoLarsCV, sklearn.decomposition.sparse_encode
- http
- //en.wikipedia.org/wiki/Least_angle_regression
Examples
>>> from sklearn import linear_model >>> clf = linear_model.LassoLars(alpha=0.01) >>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1]) ... LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True, max_iter=500, normalize=True, precompute='auto', verbose=False) >>> print clf.coef_ [ 0. -0.963257...]
Attributes
coef_ array, shape = [n_features] parameter vector (w in the fomulation formula) intercept_ float independent term in decision function. Methods
decision_function(X) Decision function of the linear model fit(X, y) Fit the model using X, y as training data. get_params([deep]) Get parameters for the 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 the estimator. - __init__(alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, copy_X=True)¶
- decision_function(X)¶
Decision function of the linear model
Parameters : X : numpy array of shape [n_samples, n_features]
Returns : C : array, shape = [n_samples]
Returns predicted values.
- fit(X, y)¶
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]
target values.
Returns : self : object
returns an instance of self.
- get_params(deep=True)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- predict(X)¶
Predict using the linear model
Parameters : X : numpy array of shape [n_samples, n_features]
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 - 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]
Training set.
y : array-like, shape = [n_samples]
Returns : z : float
- set_params(**params)¶
Set the parameters of the 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 :