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: 

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=1e3) 
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 perprocess 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_ : arraylike, shape = [n_SV] 
Indices of support vectors. 
support_vectors_ : arraylike, 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 : {arraylike, 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 : arraylike, shape (n_samples,) 
Target values (class labels in classification, real numbers in regression) 
sample_weight : arraylike, shape (n_samples,) 
Persample 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 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: 

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 oneclass model, +1 (inlier) or 1 (outlier) is returned.
Parameters: 

X : {arraylike, 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 : arraylike, 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 : arraylike, shape = (n_samples) or (n_samples, n_outputs) 
True values for X. 
sample_weight : arraylike, 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.