Naive Bayes

Naive Bayes model with Gaussian, multinomial, or kernel predictors

Naive Bayes models assume that observations have some multivariate distribution given class membership, but the predictor or features composing the observation are independent. This framework can accommodate a complete feature set such that an observation is a set of multinomial counts.

To train a naive Bayes model, use fitcnb in the command-line interface. After training, predict labels or estimate posterior probabilities by passing the model and predictor data to predict.

Apps

Classification Learner

Train models to classify data using supervised machine learning

Blocks

ClassificationNaiveBayes Predict

Classify observations using naive Bayes model (Since R2023b)

Functions

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Create Naive Bayes Model

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

Interpret Naive Bayes Model

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

Cross-Validate

`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

Measure Performance

`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

Classify Observations

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

Convert to Incremental Learner

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

Objects

ClassificationPartitionedModel Cross-validated classification model

Classes

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

Topics

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.