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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, positive=False)[source] -
Cross-validated Least Angle Regression model
Read more in the User Guide.
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).
positive : boolean (default=False)
Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default.
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression. This parameter is ignored when
fit_interceptis set to False. When the regressors are normalized, note that this makes the hyperparameters learnt more robust and almost independent of the number of samples. The same property is not valid for standardized data. However, if you wish to standardize, please usepreprocessing.StandardScalerbefore callingfiton an estimator withnormalize=False.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 : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs,
KFoldis used.Refer User Guide for the various cross-validation strategies that can be used here.
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 CPUseps : 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)n_iter_ : array-like or int
the number of iterations run by Lars with the optimal alpha.
See also
Methods
decision_function(\*args, \*\*kwargs)DEPRECATED: and will be removed in 0.19. 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, positive=False)[source]
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decision_function(*args, **kwargs)[source] -
DEPRECATED: and will be removed in 0.19.
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.
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fit(X, y)[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,)
Target values.
Returns: self : object
returns an instance of self.
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get_params(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.
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predict(X)[source] -
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.
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score(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 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 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.
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.
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set_params(**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 :
linear_model.LarsCV()
2017-01-15 04:23:20
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