type t =
[ `BaseEstimator
| `ClassifierMixin
| `MetaEstimatorMixin
| `Object
| `OneVsOneClassifier ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tOne-vs-one multiclass strategy
This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers, this method is usually slower than one-vs-the-rest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don't scale well with `n_samples`. This is because each individual learning problem only involves a small subset of the data whereas, with one-vs-the-rest, the complete dataset is used `n_classes` times.
Read more in the :ref:`User Guide <ovo_classification>`.
Parameters ---------- estimator : estimator object An estimator object implementing :term:`fit` and one of :term:`decision_function` or :term:`predict_proba`.
n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
Attributes ---------- estimators_ : list of ``n_classes * (n_classes - 1) / 2`` estimators Estimators used for predictions.
classes_ : numpy array of shape n_classes Array containing labels.
n_classes_ : int Number of classes
pairwise_indices_ : list, length = ``len(estimators_)``, or ``None`` Indices of samples used when training the estimators. ``None`` when ``estimator`` does not have ``_pairwise`` attribute.
Decision function for the OneVsOneClassifier.
The decision values for the samples are computed by adding the normalized sum of pair-wise classification confidence levels to the votes in order to disambiguate between the decision values when the votes for all the classes are equal leading to a tie.
Parameters ---------- X : array-like of shape (n_samples, n_features)
Returns ------- Y : array-like of shape (n_samples, n_classes)
Fit underlying estimators.
Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data.
y : array-like of shape (n_samples,) Multi-class targets.
Returns ------- self
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
val partial_fit :
?classes:[> `ArrayLike ] Np.Obj.t ->
x:[> `Spmatrix ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tPartially fit underlying estimators
Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration, where the first call should have an array of all target variables.
Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data.
y : array-like of shape (n_samples,) Multi-class targets.
classes : array, shape (n_classes, ) Classes across all calls to partial_fit. Can be obtained via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls.
Returns ------- self
Estimate the best class label for each sample in X.
This is implemented as ``argmax(decision_function(X), axis=1)`` which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair.
Parameters ---------- X : (sparse) array-like of shape (n_samples, n_features) Data.
Returns ------- y : numpy array of shape n_samples Predicted multi-class targets.
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val estimators_ : t -> Py.Object.tAttribute estimators_: get value or raise Not_found if None.
val estimators_opt : t -> Py.Object.t optionAttribute estimators_: get value as an option.
Attribute classes_: get value or raise Not_found if None.
val n_classes_ : t -> intAttribute n_classes_: get value or raise Not_found if None.
val n_classes_opt : t -> int optionAttribute n_classes_: get value as an option.
val pairwise_indices_ : t -> Py.Object.tAttribute pairwise_indices_: get value or raise Not_found if None.
val pairwise_indices_opt : t -> Py.Object.t optionAttribute pairwise_indices_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.