6.2.8. scikits.learn.linear_model.LassoLARS¶
- class scikits.learn.linear_model.LassoLARS(alpha=1.0, fit_intercept=True, verbose=False)¶
Lasso model fit with Least Angle Regression a.k.a. LARS
It is a Linear Model trained with an L1 prior as regularizer. lasso).
Parameters : alpha : float, optional
Constant that multiplies the L1 term. Defaults to 1.0
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).
References
http://en.wikipedia.org/wiki/Least_angle_regression
Examples
>>> from scikits.learn import linear_model >>> clf = linear_model.LassoLARS(alpha=0.1) >>> clf.fit([[-1,1], [0, 0], [1, 1]], [-1, 0, -1]) LassoLARS(alpha=0.1, verbose=False, fit_intercept=True) >>> print clf.coef_ [ 0. 0.08350342]
Attributes
coef_ array, shape = [n_features] parameter vector (w in the fomulation formula) intercept_ float independent term in decision function. Methods
fit(X, y[, normalize, max_features, ...]) Fit the model using X, y as training data. predict(X) Predict using the linear model score(X, y) Returns the coefficient of determination of the prediction - __init__(alpha=1.0, fit_intercept=True, verbose=False)¶
- fit(X, y, normalize=True, max_features=None, precompute='auto', overwrite_X=False, **params)¶
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.
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.
Returns : self : object
returns an instance of self.
- predict(X)¶
Predict using the linear model
Parameters : X : numpy array of shape [n_samples, n_features]
Returns : C : array, shape = [n_samples]
Returns predicted values.
- score(X, y)¶
Returns the coefficient of determination of the prediction
Parameters : X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Returns : z : float