package sklearn

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sklearn
- Sklearn

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type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?n_jobs:[ `Int of int | `None ] ->
  estimator:Py.Object.t ->
  unit ->
  t

One-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.

val decision_function : x:Ndarray.t -> t -> Ndarray.t

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)

val fit : x:Py.Object.t -> y:Ndarray.t -> t -> t

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

val get_params : ?deep:bool -> t -> Py.Object.t

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:Ndarray.t ->
  x:Py.Object.t ->
  y:Ndarray.t ->
  t ->
  Py.Object.t

Partially 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

val predict : x:Py.Object.t -> t -> Ndarray.t

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:Ndarray.t ->
  x:Ndarray.t ->
  y:Ndarray.t ->
  t ->
  float

Return 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 -> t -> t

Set 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.t

Attribute estimators_: see constructor for documentation

val classes_ : t -> Ndarray.t

Attribute classes_: see constructor for documentation

val n_classes_ : t -> int

Attribute n_classes_: see constructor for documentation

val pairwise_indices_ : t -> Py.Object.t

Attribute pairwise_indices_: see constructor for documentation

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.