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Showing content from http://scikit-learn.sourceforge.net/dev/developers/../modules/generated/sklearn.svm.LinearSVR.html below:

sklearn.svm.LinearSVR — scikit-learn 0.17.dev0 documentation

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.


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