sklearn.svm.NuSVR

class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel=’rbf’, degree=3, gamma=’auto_deprecated’, 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:

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. C : float, optional (default=1.0) Penalty parameter C of the error term. 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’. Current default is ‘auto’ which uses 1 / n_features, if gamma='scale' is passed then it uses 1 / (n_features * X.std()) as value of gamma. The current default of gamma, ‘auto’, will change to ‘scale’ in version 0.22. ‘auto_deprecated’, a deprecated version of ‘auto’ is used as a default indicating that no explicit value of gamma was passed. 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(gamma='scale', C=1.0, nu=0.1)
>>> clf.fit(X, y)  
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma='scale',
      kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
      verbose=False)

Methods

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_deprecated’, coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1) [source]

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

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 (inlier) or -1 (outlier) 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 residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The 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. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. 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