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.
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.
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)
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 :
