朴素贝叶斯

具有高斯预测变量、多项预测变量或核预测变量的朴素贝叶斯模型

朴素贝叶斯模型假设在给定类成员关系的情况下，观测值具有某种多元分布，但构成观测值的预测变量或特征是彼此独立的。此框架可以容纳完整的特征集，这样一个观测值即为一个多项计数集。

要训练朴素贝叶斯模型，可以在命令行界面中使用 fitcnb。训练模型后，可将模型和预测变量数据传递给 predict，以预测标签或估计后验概率。

App

使用有监督的机器学习训练模型以对数据进行分类

Classify observations using naive Bayes model (自 R2023b 起)

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

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

`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

`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

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

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

ClassificationPartitionedModel Cross-validated classification model

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

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.
分类
此示例说明如何使用判别分析、朴素贝叶斯分类器和决策树进行分类。
Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.