Repository: scikit-learn/scikit-learn
File: sklearn/svm/_classes.py (Python)

Code before:
          1 / (n_features * X.var()) as value of gamma,
        - if 'auto', uses 1 / n_features
        - if float, must be non-negative.

        .. versionchanged:: 0.22
           The default value of ``gamma`` changed from 'auto' to 'scale'.

    coef0 : float, default=0.0
        Independent term in kernel function.
        It is only significant in 'poly' and 'sigmoid'.

    shrinking : bool, default=True
        Whether to use the shrinking heuristic.
        See the :ref:`User Guide <shrinking_svm>`.

    probability : bool, default=False
        Whether to enable probability estimates. This must be enabled prior
        to calling `fit`, will slow down that method as it internally uses
        5-fold cross-validation, and `predict_proba` may be inconsistent with
        `predict`. Read more in the :ref:`User Guide <scores_probabilities>`.

        ..deprecated:: 1.8
          Parameter `probability` will be removed in 1.10.
          Use `CalibratedClassifierCV(SVC(), ensemble=False)` instead of
          `SVC(probability=True)`.

    tol : float, default=1e-3
        Tolerance for stopping criterion.

    cache_size : float, default=200
        Specify the size of the kernel cache (in MB).

    class_weight : dict or 'balanced', default=None
        Set the parameter C of class i to class_weight[i]*C for
        SVC. If not given, all classes are supposed to have
        weight one.
        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``.

    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, default=-1
        Hard limit on iterations within solver, or -1 for no limit.

    decision_function_shape : {'ovo', 'ovr'}, default='ovr'
        Whether to return a one-vs-rest ('ovr') decision function of shape
        (n_samples, n_classes) as all other classifiers, or the original

Review comment (suggestion):
```suggestion
        ..deprecated:: 1.9
          The `probability` parameter is deprecated and will be removed in 1.11.
          Use `CalibratedClassifierCV(SVC(), ensemble=False)` instead of
          `SVC(probability=True)`.

```

Code after:
          1 / (n_features * X.var()) as value of gamma,
        - if 'auto', uses 1 / n_features
        - if float, must be non-negative.

        .. versionchanged:: 0.22
           The default value of ``gamma`` changed from 'auto' to 'scale'.

    coef0 : float, default=0.0
        Independent term in kernel function.
        It is only significant in 'poly' and 'sigmoid'.

    shrinking : bool, default=True
        Whether to use the shrinking heuristic.
        See the :ref:`User Guide <shrinking_svm>`.

    probability : bool, default=False
        Whether to enable probability estimates. This must be enabled prior
        to calling `fit`, will slow down that method as it internally uses
        5-fold cross-validation, and `predict_proba` may be inconsistent with
        `predict`. Read more in the :ref:`User Guide <scores_probabilities>`.

        ..deprecated:: 1.9
          The `probability` parameter is deprecated and will be removed in 1.11.
          Use `CalibratedClassifierCV(SVC(), ensemble=False)` instead of
          `SVC(probability=True)`.

    tol : float, default=1e-3
        Tolerance for stopping criterion.

    cache_size : float, default=200
        Specify the size of the kernel cache (in MB).

    class_weight : dict or 'balanced', default=None
        Set the parameter C of class i to class_weight[i]*C for
        SVC. If not given, all classes are supposed to have
        weight one.
        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``.

    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, default=-1
        Hard limit on iterations within solver, or -1 for no limit.

    decision_function_shape : {'ovo', 'ovr'}, default='ovr'
        Whether to return a one-vs-rest ('ovr') decision function of shape
        (n_samples, n_classes) as all other classifiers, or the original