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 epsilonSVR.
The implementation is based on libsvm.
Read more in the User Guide.
 Parameters
 nufloat, default=0.5
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, default=1.0
Penalty parameter C of the error term.
 kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, 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, default=3
Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
 gamma{‘scale’, ‘auto’} or float, 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, default=0.0
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
 shrinkingbool, default=True
Whether to use the shrinking heuristic. See the User Guide.
 tolfloat, default=1e3
Tolerance for stopping criterion.
 cache_sizefloat, default=200
Specify the size of the kernel cache (in MB).
 verbosebool, default=False
Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
 max_iterint, default=1
Hard limit on iterations within solver, or 1 for no limit.
 Attributes
 class_weight_ndarray of shape (n_classes,)
Multipliers of parameter C for each class. Computed based on the
class_weight
parameter. coef_ndarray of 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 fromdual_coef_
andsupport_vectors_
. dual_coef_ndarray of shape (1, n_SV)
Coefficients of the support vector in the decision function.
 fit_status_int
0 if correctly fitted, 1 otherwise (will raise warning)
 intercept_ndarray of shape (1,)
Constants in decision function.
 n_support_ndarray of shape (n_classes,), dtype=int32
Number of support vectors for each class.
 shape_fit_tuple of int of shape (n_dimensions_of_X,)
Array dimensions of training vector
X
. support_ndarray of shape (n_SV,)
Indices of support vectors.
 support_vectors_ndarray of shape (n_SV, n_features)
Support vectors.
See also
References
 1
 2
Examples
>>> from sklearn.svm import NuSVR >>> from sklearn.pipeline import make_pipeline >>> from sklearn.preprocessing import StandardScaler >>> 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) >>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1)) >>> regr.fit(X, y) Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvr', NuSVR(nu=0.1))])
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])Return the coefficient of determination \(R^2\) of the prediction.
set_params
(**params)Set the parameters of this estimator.

fit
(X, y, sample_weight=None)[source]¶ Fit the SVM model according to the given training data.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)
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).
 yarraylike of shape (n_samples,)
Target values (class labels in classification, real numbers in regression).
 sample_weightarraylike of shape (n_samples,), default=None
Persample 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 Cordered 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
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

predict
(X)[source]¶ Perform regression on samples in X.
For an oneclass model, +1 (inlier) or 1 (outlier) is returned.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
 Returns
 y_predndarray of shape (n_samples,)

score
(X, y, sample_weight=None)[source]¶ Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1  \frac{u}{v})\), 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
 Xarraylike 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 with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
\(R^2\) of
self.predict(X)
wrt.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). 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
 selfestimator instance
Estimator instance.