Class SVRScikitsLearnNode

Epsilon-Support Vector Regression.

This node has been automatically generated by wrapping the ``sklearn.svm.classes.SVR`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

The free parameters in the model are C and epsilon.

The implementation is based on libsvm.

Read more in the :ref:`User Guide <svm_regression>`.

**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 the polynomial kernel function ('poly').
    Ignored by all other kernels.

gamma : float, optional (default='auto')
    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
    If gamma is 'auto' then 1/n_features will be used instead.

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=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.

**Attributes**

``support_`` : array-like, shape = [n_SV]
    Indices of support vectors.

``support_vectors_`` : array-like, 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.

**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) #doctest: +NORMALIZE_WHITESPACE
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='auto',
    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

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.

Overrides: object.__init__

Perform regression on samples in X.

This node has been automatically generated by wrapping the sklearn.svm.classes.SVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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

Parameters

X : {array-like, 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,)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the SVM model according to the given training data.

This node has been automatically generated by wrapping the sklearn.svm.classes.SVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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. For kernel="precomputed", the expected shape of X is (n_samples, n_samples).

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.

Overrides: Node.stop_training