class sklearn.svm.SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=- 1) [source]
 
Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to datasets with more than a couple of 10000 samples. For large datasets consider using LinearSVR or SGDRegressor instead, possibly after a Nystroem transformer. Read more in the User Guide.  Parameters 
 
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’.  
tolfloat, default=1e-3 

Tolerance for stopping criterion.  
Cfloat, default=1.0 

Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.  
epsilonfloat, default=0.1 

Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value.  
shrinkingbool, default=True 

Whether to use the shrinking heuristic. See the User Guide.  
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 per-process 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 from dual_coef_ and support_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  
NuSVR


Support Vector Machine for regression implemented using libsvm using a parameter to control the number of support vectors.  
LinearSVR


Scalable Linear Support Vector Machine for regression implemented using liblinear.    References  
1  
LIBSVM: A Library for Support Vector Machines  
2  
Platt, John (1999). “Probabilistic outputs for support vector machines and comparison to regularizedlikelihood methods.”   Examples >>> from sklearn.svm import SVR
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))
>>> regr.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('svr', SVR(epsilon=0.2))])
 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{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 
   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.     
  
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,) 
   
  
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 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) wrt. 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.