Class LinearSVRScikitsLearnNode

Linear Support Vector Regression.

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

Similar to SVR with parameter kernel='linear', but implemented in terms of
liblinear rather than libsvm, so it has more flexibility in the choice of
penalties and loss functions and should scale better to large numbers of
samples.

This class supports both dense and sparse input.

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

**Parameters**

C : float, optional (default=1.0)
    Penalty parameter C of the error term. The penalty is a squared
    l2 penalty. The bigger this parameter, the less regularization is used.

loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive' (default='epsilon_insensitive')
    Specifies the loss function. 'l1' is the epsilon-insensitive loss
    (standard SVR) while 'l2' is the squared epsilon-insensitive loss.

epsilon : float, optional (default=0.1)
    Epsilon parameter in the epsilon-insensitive loss function. Note
    that the value of this parameter depends on the scale of the target
    variable y. If unsure, set ``epsilon=0``.

dual : bool, (default=True)
    Select the algorithm to either solve the dual or primal
    optimization problem. Prefer dual=False when n_samples > n_features.

tol : float, optional (default=1e-4)
    Tolerance for stopping criteria.

fit_intercept : boolean, optional (default=True)
    Whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations
    (i.e. data is expected to be already centered).

intercept_scaling : float, optional (default=1)
    When self.fit_intercept is True, instance vector x becomes
    [x, self.intercept_scaling],
    i.e. a "synthetic" feature with constant value equals to
    intercept_scaling is appended to the instance vector.
    The intercept becomes intercept_scaling * synthetic feature weight
    Note! the synthetic feature weight is subject to l1/l2 regularization
    as all other features.
    To lessen the effect of regularization on synthetic feature weight
    (and therefore on the intercept) intercept_scaling has to be increased.

verbose : int, (default=0)
    Enable verbose output. Note that this setting takes advantage of a
    per-process runtime setting in liblinear that, if enabled, may not work
    properly in a multithreaded context.

random_state : int seed, RandomState instance, or None (default=None)
    The seed of the pseudo random number generator to use when
    shuffling the data.

max_iter : int, (default=1000)
    The maximum number of iterations to be run.

**Attributes**

``coef_`` : array, shape = [n_features] if n_classes == 2 else [n_classes, 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 a readonly property derived from `raw_coef_` that
    follows the internal memory layout of liblinear.

``intercept_`` : array, shape = [1] if n_classes == 2 else [n_classes]
    Constants in decision function.


See also

LinearSVC
    Implementation of Support Vector Machine classifier using the
    same library as this class (liblinear).

SVR
    Implementation of Support Vector Machine regression using libsvm:

    - the kernel can be non-linear but its SMO algorithm does not
    - scale to large number of samples as LinearSVC does.


sklearn.linear_model.SGDRegressor
    SGDRegressor can optimize the same cost function as LinearSVR
    by adjusting the penalty and loss parameters. In addition it requires
    less memory, allows incremental (online) learning, and implements
    various loss functions and regularization regimes.

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.svm.classes.LinearSVR 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)

Samples.

Returns

C : array, shape = (n_samples,)

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model according to the given training data.

This node has been automatically generated by wrapping the sklearn.svm.classes.LinearSVR 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 vector, where n_samples in the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples]

Target vector relative to X

Returns

self : object

Returns self.

Overrides: Node.stop_training