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 copy_X : boolean, optional, default True If precompute : True | False | ‘auto’ | array-like Whether to use a precomputed Gram matrix to speed up calculations. If set to 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:
For integer/None inputs, 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 eps : float, optional The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. |
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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 n_iter_ : array-like or int the number of iterations run by Lars with the optimal alpha. |
See also
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]
decision_function(*args, **kwargs)
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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. |
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Returns: |
C : array, shape = (n_samples,) Returns predicted values. |
fit(X, y)
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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. |
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Returns: |
self : object returns an instance of self. |
get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
predict(X)
[source]
Predict using the linear model
Parameters: |
X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples. |
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Returns: |
C : array, shape = (n_samples,) Returns predicted values. |
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. |
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Returns: |
score : float R^2 of self.predict(X) wrt. y. |
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 : |
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© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LarsCV.html