package sklearn

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sklearn
- Sklearn

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type tag = [

| `QuadraticDiscriminantAnalysis

]

type t =
  [ `BaseEstimator
  | `ClassifierMixin
  | `Object
  | `QuadraticDiscriminantAnalysis ]
    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 : 
  ?priors:[> `ArrayLike ] Np.Obj.t ->
  ?reg_param:float ->
  ?store_covariance:bool ->
  ?tol:float ->
  unit ->
  t

Quadratic Discriminant Analysis

A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes' rule.

The model fits a Gaussian density to each class.

.. versionadded:: 0.17 *QuadraticDiscriminantAnalysis*

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

Parameters ---------- priors : array, optional, shape = n_classes Priors on classes

reg_param : float, optional Regularizes the covariance estimate as ``(1-reg_param)*Sigma + reg_param*np.eye(n_features)``

store_covariance : boolean If True the covariance matrices are computed and stored in the `self.covariance_` attribute.

.. versionadded:: 0.17

tol : float, optional, default 1.0e-4 Threshold used for rank estimation.

.. versionadded:: 0.17

Attributes ---------- covariance_ : list of array-like of shape (n_features, n_features) Covariance matrices of each class.

means_ : array-like of shape (n_classes, n_features) Class means.

priors_ : array-like of shape (n_classes) Class priors (sum to 1).

rotations_ : list of arrays For each class k an array of shape n_features, n_k, with ``n_k = min(n_features, number of elements in class k)`` It is the rotation of the Gaussian distribution, i.e. its principal axis.

scalings_ : list of arrays For each class k an array of shape n_k. It contains the scaling of the Gaussian distributions along its principal axes, i.e. the variance in the rotated coordinate system.

classes_ : array-like, shape (n_classes,) Unique class labels.

Examples -------- >>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis >>> 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 = QuadraticDiscriminantAnalysis() >>> clf.fit(X, y) QuadraticDiscriminantAnalysis() >>> print(clf.predict([-0.8, -1])) 1

See also -------- sklearn.discriminant_analysis.LinearDiscriminantAnalysis: Linear Discriminant Analysis

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

Apply decision function to an array of samples.

Parameters ---------- X : array-like of shape (n_samples, n_features) Array of samples (test vectors).

Returns ------- C : ndarray of shape (n_samples,) or (n_samples, n_classes) Decision function values related to each class, per sample. In the two-class case, the shape is n_samples,, giving the log likelihood ratio of the positive class.

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

Fit the model according to the given training data and parameters.

.. versionchanged:: 0.19 ``store_covariances`` has been moved to main constructor as ``store_covariance``

.. versionchanged:: 0.19 ``tol`` has been moved to main constructor.

Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array, 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,)

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

Return posterior probabilities of classification.

Parameters ---------- X : array-like of shape (n_samples, n_features) Array of samples/test vectors.

Returns ------- C : ndarray of shape (n_samples, n_classes) Posterior log-probabilities of classification per class.

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

Return posterior probabilities of classification.

Parameters ---------- X : array-like of shape (n_samples, n_features) Array of samples/test vectors.

Returns ------- C : ndarray of shape (n_samples, n_classes) Posterior probabilities of classification per class.

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 covariance_ : t -> Np.Numpy.Ndarray.List.t

Attribute covariance_: get value or raise Not_found if None.

val covariance_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute covariance_: get value as an option.

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

Attribute means_: get value or raise Not_found if None.

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

Attribute means_: get value as an option.

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

Attribute priors_: get value or raise Not_found if None.

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

Attribute priors_: get value as an option.

val rotations_ : t -> Np.Numpy.Ndarray.List.t

Attribute rotations_: get value or raise Not_found if None.

val rotations_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute rotations_: get value as an option.

val scalings_ : t -> Np.Numpy.Ndarray.List.t

Attribute scalings_: get value or raise Not_found if None.

val scalings_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute scalings_: 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.