package sklearn

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sklearn
- Sklearn

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Module
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Class type

type tag = [

| `NearestCentroid

]

type t =
  [ `BaseEstimator | `ClassifierMixin | `NearestCentroid | `Object ] Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_classifier : t -> [ `ClassifierMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val create : 
  ?metric:[ `S of string | `Callable of Py.Object.t ] ->
  ?shrink_threshold:float ->
  unit ->
  t

Nearest centroid classifier.

Each class is represented by its centroid, with test samples classified to the class with the nearest centroid.

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

Parameters ---------- metric : str or callable The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by metrics.pairwise.pairwise_distances for its metric parameter. The centroids for the samples corresponding to each class is the point from which the sum of the distances (according to the metric) of all samples that belong to that particular class are minimized. If the 'manhattan' metric is provided, this centroid is the median and for all other metrics, the centroid is now set to be the mean.

.. versionchanged:: 0.19 ``metric='precomputed'`` was deprecated and now raises an error

shrink_threshold : float, default=None Threshold for shrinking centroids to remove features.

Attributes ---------- centroids_ : array-like of shape (n_classes, n_features) Centroid of each class.

classes_ : array of shape (n_classes,) The unique classes labels.

Examples -------- >>> from sklearn.neighbors import NearestCentroid >>> import numpy as np >>> X = np.array([-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]) >>> y = np.array(1, 1, 1, 2, 2, 2) >>> clf = NearestCentroid() >>> clf.fit(X, y) NearestCentroid() >>> print(clf.predict([-0.8, -1])) 1

See also -------- sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier

Notes ----- When used for text classification with tf-idf vectors, this classifier is also known as the Rocchio classifier.

References ---------- Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 6567-6572. The National Academy of Sciences.

val fit : 
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit the NearestCentroid model according to the given training data.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. Note that centroid shrinking cannot be used with sparse matrices. y : array-like of shape (n_samples,) Target values (integers)

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.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 predict : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Perform classification on an array of test vectors X.

The predicted class C for each sample in X is returned.

Parameters ---------- X : array-like of shape (n_samples, n_features)

Returns ------- C : ndarray of shape (n_samples,)

Notes ----- If the metric constructor parameter is 'precomputed', X is assumed to be the distance matrix between the data to be predicted and ``self.centroids_``.

val score : 
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.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 -> [> tag ] Obj.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 centroids_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute centroids_: get value or raise Not_found if None.

val centroids_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute centroids_: get value as an option.

val classes_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute classes_: get value or raise Not_found if None.

val classes_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute classes_: get value as an option.

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.