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sklearn.svm.SVR

class sklearn.svm.SVR(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, probability=False, cache_size=200, verbose=False, max_iter=-1, random_state=None)

epsilon-Support Vector Regression.

The free parameters in the model are C and epsilon.

The implementations is a based on libsvm.

Parameters:

C : float, optional (default=1.0)

penalty parameter C of the error term.

epsilon : float, optional (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.

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 kernel function is significant only in poly, rbf, sigmoid

gamma : float, optional (default=0.0)

kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features will be taken.

coef0 : float, optional (default=0.0)

independent term in kernel function. It is only significant in poly/sigmoid.

probability: boolean, optional (default=False) :

Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.

shrinking: boolean, optional (default=True) :

Whether to use the shrinking heuristic.

tol : float, optional (default=1e-3)

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 per-process 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.

random_state : int seed, RandomState instance, or None (default)

The seed of the pseudo random number generator to use when shuffling the data for probability estimaton.

See also

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

Examples

>>> from sklearn.svm import SVR
>>> 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 = SVR(C=1.0, epsilon=0.2)
>>> clf.fit(X, y) 
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma=0.0,
    kernel='rbf', max_iter=-1, probability=False, random_state=None,
    shrinking=True, tol=0.001, verbose=False)

Attributes

support_ array-like, shape = [n_SV] Index of support vectors.
support_vectors_ array-like, shape = [nSV, n_features] Support vectors.
dual_coef_ array, shape = [n_classes-1, n_SV] Coefficients of the support vector in the decision function.
coef_ array, shape = [n_classes-1, n_features]

Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.

coef_ is readonly property derived from dual_coef_ and support_vectors_

intercept_ array, shape = [n_class * (n_class-1) / 2] Constants in decision function.

Methods

decision_function(X) Distance of the samples X to the separating hyperplane.
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__(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, probability=False, cache_size=200, verbose=False, max_iter=-1, random_state=None)
decision_function(X)

Distance of the samples X to the separating hyperplane.

Parameters:

X : array-like, shape = [n_samples, n_features]

Returns:

X : array-like, shape = [n_samples, n_class * (n_class-1) / 2]

Returns the decision function of the sample for each class in the model.

fit(X, y, sample_weight=None)

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.

y : array-like, shape (n_samples,)

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

sample_weight : array-like, shape (n_samples,)

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

Returns:

self : object

Returns self.

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)

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)

Perform regression on samples in X.

For an one-class model, +1 or -1 is returned.

Parameters:X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns:y_pred : array, shape (n_samples,)
score(X, y, sample_weight=None)

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

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples,)

True values for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

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

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :
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