sklearn.linear_model.LarsCV
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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=None, eps=2.220446049250313e-16, copy_X=True)
[source] -
Cross-validated Least Angle Regression model.
See glossary entry for cross-validation estimator.
Read more in the User Guide.
- Parameters
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fit_interceptbool, default=True
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whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
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verbosebool or int, default=False
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Sets the verbosity amount.
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max_iterint, default=500
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Maximum number of iterations to perform.
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normalizebool, default=True
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This parameter is ignored when
fit_intercept
is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please useStandardScaler
before callingfit
on an estimator withnormalize=False
. -
precomputebool, ‘auto’ or array-like , default=’auto’
-
Whether to use a precomputed Gram matrix to speed up calculations. If set to
'auto'
let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X. -
cvint, cross-validation generator or an iterable, default=None
-
Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds.
- CV splitter,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs,
KFold
is used.Refer User Guide for the various cross-validation strategies that can be used here.
Changed in version 0.22:
cv
default value if None changed from 3-fold to 5-fold. -
max_n_alphasint, default=1000
-
The maximum number of points on the path used to compute the residuals in the cross-validation
-
n_jobsint or None, default=None
-
Number of CPUs to use during the cross validation.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details. -
epsfloat, default=np.finfo(float).eps
-
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. -
copy_Xbool, default=True
-
If
True
, X will be copied; else, it may be overwritten.
-
- Attributes
-
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active_list of length n_alphas or list of such lists
-
Indices of active variables at the end of the path. If this is a list of lists, the outer list length is
n_targets
. -
coef_array-like of shape (n_features,)
-
parameter vector (w in the formulation formula)
-
intercept_float
-
independent term in decision function
-
coef_path_array-like of shape (n_features, n_alphas)
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the varying values of the coefficients along the path
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alpha_float
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the estimated regularization parameter alpha
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alphas_array-like of shape (n_alphas,)
-
the different values of alpha along the path
-
cv_alphas_array-like of shape (n_cv_alphas,)
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all the values of alpha along the path for the different folds
-
mse_path_array-like of 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
-
lars_path, LassoLars,
LassoLarsCV
Examples
>>> from sklearn.linear_model import LarsCV >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_samples=200, noise=4.0, random_state=0) >>> reg = LarsCV(cv=5).fit(X, y) >>> reg.score(X, y) 0.9996... >>> reg.alpha_ 0.0254... >>> reg.predict(X[:1,]) array([154.0842...])
Methods
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])Return the coefficient of determination \(R^2\) of the prediction.
set_params
(**params)Set the parameters of this estimator.
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fit(X, y)
[source] -
Fit the model using X, y as training data.
- Parameters
-
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Xarray-like of shape (n_samples, n_features)
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Training data.
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yarray-like of shape (n_samples,)
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Target values.
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- Returns
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selfobject
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returns an instance of self.
-
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get_params(deep=True)
[source] -
Get parameters for this estimator.
- Parameters
-
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deepbool, default=True
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If True, will return the parameters for this estimator and contained subobjects that are estimators.
-
- Returns
-
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paramsdict
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Parameter names mapped to their values.
-
-
predict(X)
[source] -
Predict using the linear model.
- Parameters
-
-
Xarray-like or sparse matrix, shape (n_samples, n_features)
-
Samples.
-
- Returns
-
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Carray, shape (n_samples,)
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Returns predicted values.
-
-
score(X, y, sample_weight=None)
[source] -
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true - y_pred) ** 2).sum()
and \(v\) is the total sum of squares((y_true - y_true.mean()) ** 2).sum()
. The 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters
-
-
Xarray-like of shape (n_samples, n_features)
-
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator. -
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
-
True values for
X
. -
sample_weightarray-like of shape (n_samples,), default=None
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Sample weights.
-
- Returns
-
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scorefloat
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\(R^2\) of
self.predict(X)
wrt.y
.
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Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
-
set_params(**params)
[source] -
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
-
-
**paramsdict
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Estimator parameters.
-
- Returns
-
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selfestimator instance
-
Estimator instance.
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© 2007–2020 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.linear_model.LarsCV.html