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Module `Naive_bayes.GaussianNB`Source

Sourcetype tag = [

| `GaussianNB

]

Sourcetype t = [ `BaseEstimator | `ClassifierMixin | `GaussianNB | `Object ] Obj.t

Sourceval of_pyobject : Py.Object.t -> t

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

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

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

Source

val create : 
  ?priors:[> `ArrayLike ] Np.Obj.t ->
  ?var_smoothing:float ->
  unit ->
  t

Gaussian Naive Bayes (GaussianNB)

Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

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

Parameters ---------- priors : array-like of shape (n_classes,) Prior probabilities of the classes. If specified the priors are not adjusted according to the data.

var_smoothing : float, default=1e-9 Portion of the largest variance of all features that is added to variances for calculation stability.

.. versionadded:: 0.20

Attributes ---------- class_count_ : ndarray of shape (n_classes,) number of training samples observed in each class.

class_prior_ : ndarray of shape (n_classes,) probability of each class.

classes_ : ndarray of shape (n_classes,) class labels known to the classifier

epsilon_ : float absolute additive value to variances

sigma_ : ndarray of shape (n_classes, n_features) variance of each feature per class

theta_ : ndarray of shape (n_classes, n_features) mean of each feature per class

Examples -------- >>> 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) >>> from sklearn.naive_bayes import GaussianNB >>> clf = GaussianNB() >>> clf.fit(X, Y) GaussianNB() >>> print(clf.predict([-0.8, -1])) 1 >>> clf_pf = GaussianNB() >>> clf_pf.partial_fit(X, Y, np.unique(Y)) GaussianNB() >>> print(clf_pf.predict([-0.8, -1])) 1

Source

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

Fit Gaussian Naive Bayes according to X, y

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

y : array-like of shape (n_samples,) Target values.

sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted).

.. versionadded:: 0.17 Gaussian Naive Bayes supports fitting with *sample_weight*.

Returns ------- self : object

Sourceval 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.

Source

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

Incremental fit on a batch of samples.

This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

This is especially useful when the whole dataset is too big to fit in memory at once.

This method has some performance and numerical stability overhead, hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.

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

y : array-like of shape (n_samples,) Target values.

classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector.

Must be provided at the first call to partial_fit, can be omitted in subsequent calls.

sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted).

.. versionadded:: 0.17

Returns ------- self : object

Source

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

Perform classification on an array of test vectors X.

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

Returns ------- C : ndarray of shape (n_samples,) Predicted target values for X

Source

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

Return log-probability estimates for the test vector X.

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

Returns ------- C : array-like of shape (n_samples, n_classes) Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`.

Source

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

Return probability estimates for the test vector X.

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

Returns ------- C : array-like of shape (n_samples, n_classes) Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`.

Source

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.

Sourceval 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.

Sourceval class_count_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute class_count_: get value or raise Not_found if None.

Sourceval class_count_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute class_count_: get value as an option.

Sourceval class_prior_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute class_prior_: get value or raise Not_found if None.

Sourceval class_prior_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute class_prior_: get value as an option.

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

Attribute classes_: get value or raise Not_found if None.

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

Attribute classes_: get value as an option.

Sourceval epsilon_ : t -> float

Attribute epsilon_: get value or raise Not_found if None.

Sourceval epsilon_opt : t -> float option

Attribute epsilon_: get value as an option.

Sourceval sigma_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute sigma_: get value or raise Not_found if None.

Sourceval sigma_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute sigma_: get value as an option.

Sourceval theta_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute theta_: get value or raise Not_found if None.

Sourceval theta_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute theta_: get value as an option.

Sourceval to_string : t -> string

Print the object to a human-readable representation.

Sourceval show : t -> string

Print the object to a human-readable representation.

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

Pretty-print the object to a formatter.

package sklearn

Module Naive_bayes.GaussianNBSource

Module `Naive_bayes.GaussianNB`Source