type t = [ `BaseEstimator | `CategoricalNB | `ClassifierMixin | `Object ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?alpha:float ->
?fit_prior:bool ->
?class_prior:[ `Size of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
unit ->
tNaive Bayes classifier for categorical features
The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.
Read more in the :ref:`User Guide <categorical_naive_bayes>`.
Parameters ---------- alpha : float, optional (default=1.0) Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
fit_prior : boolean, optional (default=True) Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
class_prior : array-like, size (n_classes,), optional (default=None) Prior probabilities of the classes. If specified the priors are not adjusted according to the data.
Attributes ---------- category_count_ : list of arrays, len n_features Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the number of samples encountered for each class and category of the specific feature.
class_count_ : array, shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.
class_log_prior_ : array, shape (n_classes, ) Smoothed empirical log probability for each class.
classes_ : array, shape (n_classes,) Class labels known to the classifier
feature_log_prob_ : list of arrays, len n_features Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the empirical log probability of categories given the respective feature and class, ``P(x_i|y)``.
n_features_ : int Number of features of each sample.
Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array(1, 2, 3, 4, 5, 6) >>> from sklearn.naive_bayes import CategoricalNB >>> clf = CategoricalNB() >>> clf.fit(X, y) CategoricalNB() >>> print(clf.predict(X2:3)) 3
val fit :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit Naive Bayes classifier according to X, y
Parameters ---------- X : array-like, sparse matrix, shape = n_samples, n_features Training vectors, where n_samples is the number of samples and n_features is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, ..., n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder.
y : array-like, shape = n_samples Target values.
sample_weight : array-like, shape = n_samples, (default=None) Weights applied to individual samples (1. for unweighted).
Returns ------- self : object
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:[> `ArrayLike ] Np.Obj.t ->
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tIncremental 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 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, sparse matrix, shape = n_samples, n_features Training vectors, where n_samples is the number of samples and n_features is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, ..., n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder.
y : array-like, shape = n_samples Target values.
classes : array-like, 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, shape = n_samples, (default=None) Weights applied to individual samples (1. for unweighted).
Returns ------- self : object
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
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_`.
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_`.
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn 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 -> tSet 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 category_count_ : t -> Py.Object.tAttribute category_count_: get value or raise Not_found if None.
val category_count_opt : t -> Py.Object.t optionAttribute category_count_: get value as an option.
Attribute class_count_: get value or raise Not_found if None.
Attribute class_count_: get value as an option.
Attribute class_log_prior_: get value or raise Not_found if None.
Attribute class_log_prior_: get value as an option.
Attribute classes_: get value or raise Not_found if None.
val feature_log_prob_ : t -> Py.Object.tAttribute feature_log_prob_: get value or raise Not_found if None.
val feature_log_prob_opt : t -> Py.Object.t optionAttribute feature_log_prob_: get value as an option.
val n_features_ : t -> intAttribute n_features_: get value or raise Not_found if None.
val n_features_opt : t -> int optionAttribute n_features_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.