package sklearn

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package sklearn

sklearn
- Sklearn

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Class type

type tag = [

| `ClassifierMixin

]

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

val of_pyobject : Py.Object.t -> t

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

val create : unit -> t

Mixin class for all classifiers in scikit-learn.

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 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.