Class LinearSVC

sklearn/svm/_classes.py:44–364

Linear Support Vector Classification. Similar to SVC 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. The main

class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
    """Linear Support Vector Classification.

    Similar to SVC 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.

    The main differences between :class:`~sklearn.svm.LinearSVC` and
    :class:`~sklearn.svm.SVC` lie in the loss function used by default, and in
    the handling of intercept regularization between those two implementations.

    This class supports both dense and sparse input and the multiclass support
    is handled according to a one-vs-the-rest scheme.

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

    Parameters
    ----------
    penalty : {'l1', 'l2'}, default='l2'
        Specifies the norm used in the penalization. The 'l2'
        penalty is the standard used in SVC. The 'l1' leads to ``coef_``
        vectors that are sparse.

    loss : {'hinge', 'squared_hinge'}, default='squared_hinge'
        Specifies the loss function. 'hinge' is the standard SVM loss
        (used e.g. by the SVC class) while 'squared_hinge' is the
        square of the hinge loss. The combination of ``penalty='l1'``
        and ``loss='hinge'`` is not supported.

    dual : "auto" or bool, default="auto"
        Select the algorithm to either solve the dual or primal
        optimization problem. Prefer dual=False when n_samples > n_features.
        `dual="auto"` will choose the value of the parameter automatically,
        based on the values of `n_samples`, `n_features`, `loss`, `multi_class`
        and `penalty`. If `n_samples` < `n_features` and optimizer supports
        chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,
        otherwise it will be set to False.

        .. versionchanged:: 1.3
           The `"auto"` option is added in version 1.3 and will be the default
           in version 1.5.

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

    C : float, default=1.0
        Regularization parameter. The strength of the regularization is
        inversely proportional to C. Must be strictly positive.
        For an intuitive visualization of the effects of scaling
        the regularization parameter C, see
        :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.

    multi_class : {'ovr', 'crammer_singer'}, default='ovr'
        Determines the multi-class strategy if `y` contains more than
        two classes.
        ``"ovr"`` trains n_classes one-vs-rest classifiers, while
        ``"crammer_singer"`` optimizes a joint objective over all classes.