val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?alpha:float ->
?fit_prior:bool ->
?class_prior:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
unit ->
tNaive Bayes classifier for multinomial models
The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
Read more in the :ref:`User Guide <multinomial_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 ---------- 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
coef_ : array, shape (n_classes, n_features) Mirrors ``feature_log_prob_`` for interpreting MultinomialNB as a linear model.
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 log probability of features given a class, ``P(x_i|y)``.
intercept_ : array, shape (n_classes, ) Mirrors ``class_log_prior_`` for interpreting MultinomialNB as a linear model.
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 MultinomialNB >>> clf = MultinomialNB() >>> clf.fit(X, y) MultinomialNB() >>> print(clf.predict(X2:3)) 3
Notes ----- For the rationale behind the names `coef_` and `intercept_`, i.e. naive Bayes as a linear classifier, see J. Rennie et al. (2003), Tackling the poor assumptions of naive Bayes text classifiers, ICML.
References ---------- C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
val fit :
?sample_weight:Ndarray.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Ndarray.t ->
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
val get_params : ?deep:bool -> t -> Py.Object.tGet 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:Ndarray.t ->
?sample_weight:Ndarray.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Ndarray.t ->
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_`.
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 -> 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 feature_log_prob_: see constructor for documentation
val n_features_ : t -> intAttribute n_features_: see constructor for documentation
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 : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.