__init__(self,
input_dim=None,
output_dim=None,
dtype=None,
**kwargs)
(Constructor)
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Bayesian ridge regression
This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.BayesianRidge class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Fit a Bayesian ridge model and optimize the regularization parameters
lambda (precision of the weights) and alpha (precision of the noise).
Parameters
- X : array, shape = (n_samples, n_features)
- Training vectors.
- y : array, shape = (length)
- Target values for training vectors
- n_iter : int, optional
- Maximum number of interations. Default is 300.
- eps : float, optional
- Stop the algorithm if w has converged. Default is 1.e-3.
- alpha_1 : float, optional
- Hyper-parameter : shape parameter for the Gamma distribution prior
over the alpha parameter. Default is 1.e-6
- alpha_2 : float, optional
- Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the alpha parameter.
Default is 1.e-6.
- lambda_1 : float, optional
- Hyper-parameter : shape parameter for the Gamma distribution prior
over the lambda parameter. Default is 1.e-6.
- lambda_2 : float, optional
- Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the lambda parameter.
Default is 1.e-6
- compute_score : boolean, optional
- If True, compute the objective function at each step of the model.
Default is False
- fit_intercept : boolean, optional
- wether 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).
Default is True.
Attributes
coef_ : array, shape = (n_features)- Coefficients of the regression model (mean of distribution)
alpha_ : float- estimated precision of the noise.
lambda_ : array, shape = (n_features)- estimated precisions of the weights.
scores_ : float- if computed, value of the objective function (to be maximized)
Methods
- fit(X, y) : self
- Fit the model
- predict(X) : array
- Predict using the model.
Examples
>>> from scikits.learn import linear_model
>>> clf = linear_model.BayesianRidge()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
BayesianRidge(n_iter=300, verbose=False, lambda_1=1e-06, lambda_2=1e-06,
fit_intercept=True, eps=0.001, alpha_2=1e-06, alpha_1=1e-06,
compute_score=False)
>>> clf.predict([[1, 1]])
array([ 1.])
Notes
See examples/linear_model/plot_bayesian_ridge.py for an example.
- Overrides:
object.__init__
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