sklearn.linear_model.Lars — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.linear_model. Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)[source]¶
Least Angle Regression model a.k.a. LAR
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). verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default True 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 usesklearn.preprocessing.StandardScaler before calling fiton an estimator with normalize=False. 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. n_nonzero_coefs : int, optional Target number of non-zero coefficients. Use np.inf for no limit. 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. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. fit_path : boolean If True the full path is stored in the coef_path_ attribute. If you compute the solution for a large problem or many targets, setting fit_path to False will lead to a speedup, especially with a small alpha. 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. Deprecated since version 0.20: The option is broken and deprecated. It will be removed in v0.22. | |
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| Attributes: | alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. n_alphas is either n_nonzero_coefs or n_features, whichever is smaller. active_ : list, length = n_alphas | list of n_targets such lists Indices of active variables at the end of the path. coef_path_ : array, shape (n_features, n_alphas + 1) | list of n_targets such arrays The varying values of the coefficients along the path. It is not present if the fit_path parameter is False. coef_ : array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula). intercept_ : float | array, shape (n_targets,) Independent term in decision function. n_iter_ : array-like or int The number of iterations taken by lars_path to find the grid of alphas for each target. |
Examples
from sklearn import linear_model reg = linear_model.Lars(n_nonzero_coefs=1) reg.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111]) ... Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True, n_nonzero_coefs=1, normalize=True, positive=False, precompute='auto', verbose=False) print(reg.coef_) [ 0. -1.11...]
Methods
| fit(X, y[, Xy]) | Fit the model using X, y as training data. |
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| 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, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)[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,) or (n_samples, n_targets) Target values. Xy : array-like, shape (n_samples,) or (n_samples, n_targets), optional Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed. |
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| Returns: | self : object returns an instance of self. |
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. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
Predict using the linear model
| Parameters: | X : array_like or 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 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 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. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. 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 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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