3.2.3.1.3. sklearn.linear_model.LarsCV¶
- class sklearn.linear_model.LarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=1, eps=2.2204460492503131e-16, copy_X=True)¶
Cross-validated Least Angle Regression model
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, default False
If True, the regressors X will be normalized before regression.
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.
cv : cross-validation generator, optional
see sklearn.cross_validation. If None is passed, default to a 5-fold strategy
max_n_alphas : integer, optional
The maximum number of points on the path used to compute the residuals in the cross-validation
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If -1, use all the CPUs
eps: float, optional :
The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems.
Attributes: ``coef_`` : array, shape (n_features,)
parameter vector (w in the formulation formula)
``intercept_`` : float
independent term in decision function
``coef_path_`` : array, shape (n_features, n_alphas)
the varying values of the coefficients along the path
``alpha_`` : float
the estimated regularization parameter alpha
``alphas_`` : array, shape (n_alphas,)
the different values of alpha along the path
``cv_alphas_`` : array, shape (n_cv_alphas,)
all the values of alpha along the path for the different folds
``cv_mse_path_`` : array, shape (n_folds, n_cv_alphas)
the mean square error on left-out for each fold along the path (alpha values given by cv_alphas)
See also
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 this estimator. predict(X) Predict using the linear model score(X, y[, sample_weight]) 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, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=1, eps=2.2204460492503131e-16, copy_X=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)¶
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 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, sample_weight=None)¶
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.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
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 :