logP

Log unconditional probability density for naive Bayes classifier

Syntax

lp = logP(Mdl,tbl)
lp = logP(Mdl,X)

Description

lp = logP(Mdl,tbl) returns the log unconditional probability density of the observations (rows) in tbl using the naive Bayes model Mdl.

You can use lp to identify outliers in the training data.

example

lp = logP(Mdl,X) returns the log unconditional probability density of the observations (rows) in X using the naive Bayes model Mdl.

Input Arguments

expand all

Naive Bayes classifier, specified as a ClassificationNaiveBayes model or CompactClassificationNaiveBayes model returned by fitcnb or compact, respectively.

Sample data, specified as a table. Each row of tbl corresponds to one observation, and each column corresponds to one predictor variable. Optionally, tbl can contain additional columns for the response variable and observation weights. tbl must contain all the predictors used to train Mdl. Multi-column variables and cell arrays other than cell arrays of character vectors are not allowed.

If you trained Mdl using sample data contained in a table, then the input data for this method must also be in a table.

Data Types: table

Predictor data, specified as a numeric matrix.

Each row of X corresponds to one observation (also known as an instance or example), and each column corresponds to one variable (also known as a feature). The variables making up the columns of X must be the same as the variables that trained Mdl.

Data Types: double | single

Output Arguments

expand all

Log of the unconditional probability density of the predictors, returned as a numeric column vector. lp has as many elements as rows in X, and each element is the log probability density of the corresponding row in X.

If any rows in X contain at least one NaN, then the corresponding element of lp is NaN.

Examples

expand all

Load Fisher's iris data set.

load fisheriris
X = meas;    % Predictors
Y = species; % Response

Train a naive Bayes classifier. It is good practice to specify the class order. Assume that each predictor is conditionally normally distributed given its label.

Mdl = fitcnb(X,Y,'ClassNames',{'setosa','versicolor','virginica'});

Mdl is a trained ClassificationNaiveBayes classifier.

Compute the unconditional probability densities of the in-sample observations.

lp = logP(Mdl,X);
histogram(lp)
xlabel 'Log-unconditional probability'
ylabel 'Frequency'
title 'Histogram: Log-Unconditional Probability'

Identify indices of observations having log-unconditional probability less than -7.

idx = find(lp < -7)
idx = 3×1

    61
   118
   132

More About

expand all