La traducción de esta página aún no se ha actualizado a la versión más reciente. Haga clic aquí para ver la última versión en inglés.

Naive Bayes

Modelo Naive Bayes con predictores gaussianos, multinomiales o de kernel

Los modelos Naive Bayes suponen que las observaciones tienen alguna distribución multivariante dada la pertenencia a una clase, aunque el predictor o las características que componen la observación son independientes. Este marco puede dar cabida a un conjunto completo de características, de manera que una observación es un conjunto de recuentos multinomiales.

Para entrenar un modelo Naive Bayes, utilice fitcnb en la interfaz de línea de comandos. Tras el entrenamiento, prediga las etiquetas o calcule las probabilidades a posteriori pasando el modelo y los datos de los predictores a predict.

Apps

Classification Learner

Entrenar modelos para clasificar datos usando machine learning supervisado

Bloques

ClassificationNaiveBayes Predict

Classify observations using naive Bayes model (desde R2023b)

Funciones

expandir todo

Crear un modelo Naive Bayes

`fitcnb`	Train multiclass naive Bayes model
`compact`	Reduce size of machine learning model

Interpretar un modelo Naive Bayes

`lime`	Local interpretable model-agnostic explanations (LIME) (desde R2020b)
`partialDependence`	Compute partial dependence (desde R2020b)
`permutationImportance`	Predictor importance by permutation (desde R2024a)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (desde R2021a)

Realizar una validación cruzada

`crossval`	Cross-validate machine learning model
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldfun`	Cross-validate function for classification
`kfoldMargin`	Classification margins for cross-validated classification model
`kfoldPredict`	Classify observations in cross-validated classification model

Medir el rendimiento

`loss`	Classification loss for naive Bayes classifier
`resubLoss`	Resubstitution classification loss
`logp`	Log unconditional probability density for naive Bayes classifier
`compareHoldout`	Compare accuracies of two classification models using new data
`edge`	Classification edge for naive Bayes classifier
`margin`	Classification margins for naive Bayes classifier
`resubEdge`	Resubstitution classification edge
`resubMargin`	Resubstitution classification margin
`testckfold`	Compare accuracies of two classification models by repeated cross-validation

Clasificar observaciones

`predict`	Classify observations using naive Bayes classifier
`resubPredict`	Classify training data using trained classifier

Convertir en un modelo de aprendizaje incremental

incrementalLearner Convert naive Bayes classification model to incremental learner (desde R2021a)

Objetos

ClassificationPartitionedModel Cross-validated classification model

Clases

`ClassificationNaiveBayes`	Naive Bayes classification for multiclass classification
`CompactClassificationNaiveBayes`	Compact naive Bayes classifier for multiclass classification

Temas

Train Naive Bayes Classifiers Using Classification Learner App
Create and compare naive Bayes classifiers, and export trained models to make predictions for new data.
Supervised Learning Workflow and Algorithms
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Parametric Classification
Learn about parametric classification methods.
Naive Bayes Classification
The naive Bayes classifier is designed for use when predictors are independent of one another within each class, but it appears to work well in practice even when that independence assumption is not valid.
Plot Posterior Classification Probabilities
This example shows how to visualize classification probabilities for the Naive Bayes classification algorithm.
Classification
This example shows how to perform classification using discriminant analysis, naive Bayes classifiers, and decision trees.
Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.