package sklearn

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package sklearn

sklearn
- Sklearn

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type tag = [

| `MultinomialNB

]

type t = [ `BaseEstimator | `ClassifierMixin | `MultinomialNB | `Object ] 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 : 
  ?alpha:float ->
  ?fit_prior:bool ->
  ?class_prior:[> `ArrayLike ] Np.Obj.t ->
  unit ->
  t

Naive 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, default=1.0 Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).

fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used.

class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified the priors are not adjusted according to the data.

Attributes ---------- class_count_ : ndarray of 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_ : ndarray of shape (n_classes, ) Smoothed empirical log probability for each class.

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

coef_ : ndarray of shape (n_classes, n_features) Mirrors ``feature_log_prob_`` for interpreting MultinomialNB as a linear model.

feature_count_ : ndarray of 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_ : ndarray of shape (n_classes, n_features) Empirical log probability of features given a class, ``P(x_i|y)``.

intercept_ : ndarray of 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:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit 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 -> [> 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 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 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

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

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_`.

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_`.

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

Attribute class_count_: get value or raise Not_found if None.

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

Attribute class_count_: get value as an option.

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

Attribute class_log_prior_: get value or raise Not_found if None.

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

Attribute class_log_prior_: 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 coef_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute coef_: get value or raise Not_found if None.

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

Attribute coef_: get value as an option.

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

Attribute feature_count_: get value or raise Not_found if None.

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

Attribute feature_count_: get value as an option.

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

Attribute feature_log_prob_: get value or raise Not_found if None.

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

Attribute feature_log_prob_: get value as an option.

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

Attribute intercept_: get value or raise Not_found if None.

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

Attribute intercept_: get value as an option.

val n_features_ : t -> int

Attribute n_features_: get value or raise Not_found if None.

val n_features_opt : t -> int option

Attribute n_features_: 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 : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.