9.1.5. sklearn.svm.NuSVR

class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001)

Nu Support Vector Regression.

Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces with the parameter epsilon of SVR.

Parameters :

C : float, optional (default=1.0)

penalty parameter C of the error term.

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 (default=’rbf’)

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.

degree : int, optional (default=3)

degree of kernel function is significant only in poly, rbf, sigmoid

gamma : float, optional (default=0.0)

kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features will be taken.

coef0 : float, optional (default=0.0)

independent term in kernel function. It is only significant in poly/sigmoid.

probability: boolean, optional (default=False) :

Whether to enable probability estimates. This must be enabled prior to calling prob_predict.

shrinking: boolean, optional (default=True) :

Whether to use the shrinking heuristic.

tol: float, optional (default=1e-3) :

Tolerance for stopping criterion.

See also

NuSVC, SVR

Examples

>>> from sklearn.svm import NuSVR
>>> 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 = NuSVR(C=1.0, nu=0.1)
>>> clf.fit(X, y)
NuSVR(C=1.0, coef0=0.0, degree=3, gamma=0.2, kernel='rbf', nu=0.1,
   probability=False, shrinking=True, tol=0.001)

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.

Methods

decision_function(X)	Distance of the samples T to the separating hyperplane.
fit(X, y[, sample_weight])	Fit the SVM model according to the given training data and parameters.
predict(X)	Perform classification or regression samples in X.
predict_log_proba(T)	Compute the log likehoods each possible outcomes of samples in T.
predict_proba(X)	Compute the likehoods each possible outcomes of samples in T.
score(X, y)	Returns the coefficient of determination of the prediction
set_params(**params)	Set the parameters of the estimator.

__init__(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001)

decision_function(X)

Distance of the samples T to the separating hyperplane.

Parameters :

X : array-like, shape = [n_samples, n_features]

Returns :

X : array-like, shape = [n_samples, n_class * (n_class-1) / 2]

Returns the decision function of the sample for each class in the model.

fit(X, y, sample_weight=None, **params)

Fit the SVM model according to the given training data and parameters.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array, shape = [n_samples]

Target values. Array of floating-point numbers.

Returns :

self : object

Returns self.

predict(X)

Perform classification or regression samples in X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the function value of X calculated is returned.

For an one-class model, +1 or -1 is returned.

Parameters :X : array-like, shape = [n_samples, n_features] Returns :C : array, shape = [n_samples]

predict_log_proba(T)

Compute the log likehoods each possible outcomes of samples in T.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters :

T : array-like, shape = [n_samples, n_features]

Returns :

T : array-like, shape = [n_samples, n_classes]

Returns the log-probabilities of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.

predict_proba(X)

Compute the likehoods each possible outcomes of samples in T.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters :

X : array-like, shape = [n_samples, n_features]

Returns :

X : array-like, shape = [n_samples, n_classes]

Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.

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

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :