type t = [ `BaseEstimator | `ClassifierMixin | `ComplementNB | `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 ] ->
?norm:bool ->
unit ->
tThe Complement Naive Bayes classifier described in Rennie et al. (2003).
The Complement Naive Bayes classifier was designed to correct the 'severe assumptions' made by the standard Multinomial Naive Bayes classifier. It is particularly suited for imbalanced data sets.
Read more in the :ref:`User Guide <complement_naive_bayes>`.
Parameters ---------- alpha : float, optional (default=1.0) Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
fit_prior : boolean, optional (default=True) Only used in edge case with a single class in the training set.
class_prior : array-like, size (n_classes,), optional (default=None) Prior probabilities of the classes. Not used.
norm : boolean, optional (default=False) Whether or not a second normalization of the weights is performed. The default behavior mirrors the implementations found in Mahout and Weka, which do not follow the full algorithm described in Table 9 of the paper.
Attributes ---------- 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. Only used in edge case with a single class in the training set.
classes_ : array, shape (n_classes,) Class labels known to the classifier
feature_all_ : array, shape (n_features,) Number of samples encountered for each feature during fitting. This value is weighted by the sample weight when provided.
feature_count_ : array, shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.
feature_log_prob_ : array, shape (n_classes, n_features) Empirical weights for class complements.
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 ComplementNB >>> clf = ComplementNB() >>> clf.fit(X, y) ComplementNB() >>> print(clf.predict(X2:3)) 3
References ---------- Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In ICML (Vol. 3, pp. 616-623). https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf
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 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).
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 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).
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.
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.
Attribute feature_all_: get value or raise Not_found if None.
Attribute feature_all_: get value as an option.
Attribute feature_count_: get value or raise Not_found if None.
Attribute feature_count_: get value as an option.
Attribute feature_log_prob_: get value or raise Not_found if None.
Attribute 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.