svm.NuSVR()

class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1) [source]

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 the parameter epsilon of epsilon-SVR.

The implementation is based on libsvm.

Read more in the User Guide.

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. kernel : string, optional (default=?rbf?) Specifies the kernel type to be used in the algorithm. It must be one of ?linear?, ?poly?, ?rbf?, ?sigmoid?, ?precomputed? or a callable. If none is given, ?rbf? will be used. If a callable is given it is used to precompute the kernel matrix. degree : int, optional (default=3) Degree of the polynomial kernel function (?poly?). Ignored by all other kernels. gamma : float, optional (default=?auto?) Kernel coefficient for ?rbf?, ?poly? and ?sigmoid?. If gamma is ?auto? then 1/n_features will be used instead. coef0 : float, optional (default=0.0) Independent term in kernel function. It is only significant in ?poly? and ?sigmoid?. shrinking : boolean, optional (default=True) Whether to use the shrinking heuristic. tol : float, optional (default=1e-3) Tolerance for stopping criterion. cache_size : float, optional Specify the size of the kernel cache (in MB). verbose : bool, default: False Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context. max_iter : int, optional (default=-1) Hard limit on iterations within solver, or -1 for no limit.

Attributes:

support_ : array-like, shape = [n_SV] Indices of support vectors. support_vectors_ : array-like, shape = [nSV, n_features] Support vectors. dual_coef_ : array, shape = [1, n_SV] Coefficients of the support vector in the decision function. coef_ : array, shape = [1, n_features] Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. coef_ is readonly property derived from dual_coef_ and support_vectors_. intercept_ : array, shape = [1] Constants in decision function.

See also

NuSVC

Support Vector Machine for classification implemented with libsvm with a parameter to control the number of support vectors.

SVR

epsilon Support Vector Machine for regression implemented with libsvm.

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, cache_size=200, coef0=0.0, degree=3, gamma='auto',
      kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
      verbose=False)

Methods

decision_function(\*args, \*\*kwargs)	DEPRECATED: and will be removed in 0.19
fit(X, y[, sample_weight])	Fit the SVM model according to the given training data.
get_params([deep])	Get parameters for this estimator.
predict(X)	Perform regression on samples in X.
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__(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1) [source]

decision_function(*args, **kwargs) [source]

DEPRECATED: and will be removed in 0.19

Distance of the samples X to the separating hyperplane.

Parameters:	X : array-like, shape (n_samples, n_features) For kernel=?precomputed?, the expected shape of X is [n_samples_test, n_samples_train].
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) [source]

Fit the SVM model according to the given training data.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=?precomputed?, the expected shape of X is (n_samples, n_samples). y : array-like, shape (n_samples,) Target values (class labels in classification, real numbers in regression) sample_weight : array-like, shape (n_samples,) Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:

self : object Returns self.

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

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.
Returns:	params : mapping of string to any Parameter names mapped to their values.

predict(X) [source]

Perform regression on samples in X.

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

Parameters:	X : {array-like, sparse matrix}, shape (n_samples, n_features) For kernel=?precomputed?, the expected shape of X is (n_samples_test, n_samples_train).
Returns:	y_pred : array, shape (n_samples,)

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 regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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. 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.
Returns:	score : float R^2 of self.predict(X) wrt. y.

set_params(**params) [source]

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 :

Examples using sklearn.svm.NuSVR

Model Complexity Influence