`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.

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.

References

[1]

LIBSVM: A Library for Support Vector Machines

[2]

Platt, John (1999). “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods”

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 of the prediction.
`set_params`(**params)	Set the parameters of this estimator.

property coef_¶

Weights assigned to the features when kernel="linear".

Returns:

ndarray of shape (n_features, n_classes)

fit(X, y, sample_weight=None)[source]¶

Fit the SVM model according to the given training data.

Parameters:

X{array-like, 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).
yarray-like of shape (n_samples,): Target values (class labels in classification, real numbers in regression).
sample_weightarray-like of shape (n_samples,), default=None: Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:

selfobject: Fitted estimator.

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:

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.

property n_support_¶: Number of support vectors for each class.

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} 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,): The predicted values.

score(X, y, sample_weight=None)[source]¶

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) 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 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 with 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) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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.

Examples using `sklearn.svm.NuSVR`¶

Model Complexity Influence

sklearn.svm.NuSVR¶

Examples using sklearn.svm.NuSVR¶

`sklearn.svm`.NuSVR¶

Examples using `sklearn.svm.NuSVR`¶