classifierPlot

Plot the results of a given classifier after training

Contents

Syntax

Description

classifierPlot(classifier, DS, cPrm) plots the training results of a given classifier.

• classifier: a string specifying a classifier
  • classifier='qc' for quadratic classifier
  • classifier='nbc' for naive Bayes classifier
  • classifier='gmmc' for GMM classifier
  • classifier='linc' for linear classifier
  • classifier='knnc' for k-nearest-neighbor classifier
• DS: dataset used for training the classifier
• cPrm: parameters of the classifier

classifierPlot(classifier, DS, cPrm, mode) uses a string variable to specify the plot mode

• mode='1dPdf' for 1D PDF plot (for nbc only)
• mode='2dPdf' for 2D PDF plot
• mode='2dPosterior' for 2D posterior probability plot
• mode='decBoundary' for decision boundary plot

surfObj=classifierPlot(classifier, DS, cPrm, ...) return the surface object for plotting instead of plotting directly.

Example

1-D PDF plot for a naive Bayes classifier:

DS=prData('3classes');
classifier='nbc';
cPrm=classifierTrain(classifier, DS);
figure; classifierPlot(classifier, DS, cPrm, '1dPdf');

2-D PDF plot for a GMM classifier:

DS=prData('3classes');
classifier='gmmc';
cPrm=classifierTrain(classifier, DS);
figure; classifierPlot(classifier, DS, cPrm, '2dPdf');

2-D posterior prob. plot for a GMM classifier:

DS=prData('3classes');
classifier='gmmc';
cPrm=classifierTrain(classifier, DS);
figure; classifierPlot(classifier, DS, cPrm, '2dPosterior');

Decision boundary plot for a GMM classifier:

DS=prData('3classes');
classifier='gmmc';
cPrm=classifierTrain(classifier, DS);
figure; classifierPlot(classifier, DS, cPrm, 'decBoundary');

For KNNC

classifier='knnc';
[trainSet, testSet]=prData('3classes');
cPrm=knncTrain(trainSet);
cPrm.k=1;

Plot 2D posterior-like function:

figure; classifierPlot(classifier, trainSet, cPrm, '2dPosterior');

Plot decision boundary:

figure; classifierPlot(classifier, trainSet, cPrm, 'decBoundary');

Published with MATLAB® R2018a