8.26.1.4. 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, scale_C=True)¶
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. one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’. If none is given ‘rbf’ will be used.
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 predict_proba.
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)
scale_C : bool, default: True
Scale C with number of samples. It makes the setting of C independent of the number of samples. To match libsvm commandline one should use scale_C=False. WARNING: scale_C will disappear in version 0.12.
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=0.2, kernel='rbf', probability=False, scale_C=True, shrinking=True, tol=0.001)
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[, class_weight, sample_weight]) Fit the SVM model according to the given training data. get_params([deep]) Get parameters for the estimator predict(X) Perform classification or regression samples in X. predict_log_proba(X) Compute the log likehoods each possible outcomes of samples in X. predict_proba(X) Compute the likehoods each possible outcomes of samples in T. score(X, y) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of the 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, scale_C=True)¶
- 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, class_weight=None, 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 (integers in classification, real numbers in regression)
sample_weight : array-like, shape = [n_samples], optional
Weights applied to individual samples (1. for unweighted).
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 the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- predict(X)¶
Perform classification or regression samples in X.
For a classification model, the predicted class for each sample in X is returned. For a regression model, the function value of X calculated is returned.
For an one-class model, +1 or -1 is returned.
Parameters : X : {array-like, sparse matrix}, shape = [n_samples, n_features] Returns : C : array, shape = [n_samples]
- predict_log_proba(X)¶
Compute the log likehoods each possible outcomes of samples in X.
The model need to have probability information computed at training time: fit with attribute probability set to True.
Parameters : X : array-like, shape = [n_samples, n_features]
Returns : X : array-like, shape = [n_samples, n_classes]
Returns the log-probabilities of the sample for each class in the model, where classes are ordered by arithmetical order.
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.
- predict_proba(X)¶
Compute the likehoods each possible outcomes of samples in T.
The model need to have probability information computed at training time: fit with attribute probability set to True.
Parameters : X : array-like, shape = [n_samples, n_features]
Returns : X : array-like, shape = [n_samples, n_classes]
Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.
- score(X, y)¶
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 - 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]
Training set.
y : array-like, shape = [n_samples]
Returns : z : float
- set_params(**params)¶
Set the parameters of the 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 :