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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

分类学习器使用有监督的机器学习训练模型以对数据进行分类

函数

全部展开

fitcnbTrain multiclass naive Bayes model
compactReduce size of machine learning model
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapleyShapley values
crossvalCross-validate machine learning model
kfoldEdgeClassification edge for cross-validated classification model
kfoldLossClassification loss for cross-validated classification model
kfoldfunCross-validate function for classification
kfoldMarginClassification margins for cross-validated classification model
kfoldPredictClassify observations in cross-validated classification model
lossClassification loss for naive Bayes classifier
resubLossResubstitution classification loss
logpLog unconditional probability density for naive Bayes classifier
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge for naive Bayes classifier
marginClassification margins for naive Bayes classifier
resubEdgeResubstitution classification edge
resubMarginResubstitution classification margin
testckfoldCompare accuracies of two classification models by repeated cross-validation
predictClassify observations using naive Bayes classifier
resubPredictClassify training data using trained classifier
incrementalLearnerConvert naive Bayes classification model to incremental learner

ClassificationNaiveBayesNaive Bayes classification for multiclass classification
CompactClassificationNaiveBayesCompact naive Bayes classifier for multiclass classification
ClassificationPartitionedModelCross-validated classification model

主题

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

Categorical response data

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.