sklearn.svm.NuSVR

class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='scale', 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
nufloat, 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.

Cfloat, optional (default=1.0)

Penalty parameter C of the error term.

kernelstring, 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.

degreeint, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma{‘scale’, ‘auto’} or float, optional (default=’scale’)

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

  • if ‘auto’, uses 1 / n_features.

Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

coef0float, optional (default=0.0)

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

shrinkingboolean, optional (default=True)

Whether to use the shrinking heuristic.

tolfloat, optional (default=1e-3)

Tolerance for stopping criterion.

cache_sizefloat, optional

Specify the size of the kernel cache (in MB).

verbosebool, 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_iterint, optional (default=-1)

Hard limit on iterations within solver, or -1 for no limit.

Attributes
support_array-like of shape (n_SV)

Indices of support vectors.

support_vectors_array-like of shape (n_SV, 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.

Notes

References: LIBSVM: A Library for Support Vector Machines

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(nu=0.1)

Methods

fit(self, X, y[, sample_weight])

Fit the SVM model according to the given training data.

get_params(self[, deep])

Get parameters for this estimator.

predict(self, X)

Perform regression on samples in X.

score(self, X, y[, sample_weight])

Return the coefficient of determination R^2 of the prediction.

set_params(self, \*\*params)

Set the parameters of this estimator.
__init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, 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).

yarray-like, shape (n_samples,)

Target values (class labels in classification, real numbers in regression)

sample_weightarray-like, shape (n_samples,)

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns
selfobject

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(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

predict(self, 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_predarray, shape (n_samples,)
score(self, X, y, sample_weight=None)[source]

Return 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
Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns
scorefloat

R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(self, **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.

Parameters
**paramsdict

Estimator parameters.

Returns
selfobject

Estimator instance.

Examples using sklearn.svm.NuSVR