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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