__init__(self,
input_dim=None,
output_dim=None,
dtype=None,
**kwargs)
(Constructor)
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epsilon-Support Vector Regression.
This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVR class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
The free parameters in the model are C and epsilon.
Parameters
- nu : float, optional
- An upper bound on the fraction of training errors and a lower bound of
the fraction of support vectors. Should be in the interval (0, 1]. By
default 0.5 will be taken. Only available if impl='nu_svc'
- kernel : string, optional
- Specifies the kernel type to be used in the algorithm.
one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'.
If none is given 'rbf' will be used.
- epsilon : float
- epsilon in the epsilon-SVR model.
- degree : int, optional
- degree of kernel function
is significant only in poly, rbf, sigmoid
- gamma : float, optional
- kernel coefficient for rbf and poly, by default 1/n_features
will be taken.
- C : float, optional (default=1.0)
- penalty parameter C of the error term.
- probability: boolean, optional (False by default)
- enable probability estimates. This must be enabled prior
to calling prob_predict.
- tol: float, optional
- precision for stopping criteria
- coef0 : float, optional
- independent term in kernel function. It is only significant
in poly/sigmoid.
- cache_size: float, optional
- specify the size of the cache (in MB)
- shrinking: boolean, optional
- wether to use the shrinking heuristic.
Attributes
support_ : array-like, shape = [n_SV]- Index of support vectors.
support_vectors_ : array-like, shape = [nSV, n_features]- Support vectors.
dual_coef_ : array, shape = [n_classes-1, n_SV]- Coefficients of the support vector in the decision function.
coef_ : array, shape = [n_classes-1, n_features]- Weights asigned to the features (coefficients in the primal
problem). This is only available in the case of linear kernel.
intercept_ : array, shape = [n_class * (n_class-1) / 2]- Constants in decision function.
Examples
>>> from scikits.learn.svm import SVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = SVR(C=1.0, epsilon=0.2)
>>> clf.fit(X, y)
SVR(kernel='rbf', C=1.0, probability=False, degree=3, epsilon=0.2,
shrinking=True, tol=0.001, cache_size=100.0, coef0=0.0, nu=0.5,
gamma=0.1)
See also
NuSVR
- Overrides:
object.__init__
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