classifiers

Introduction of defining, training and evaluating classifiers

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Download the m-file from here. See http://prtools.tudelft.nl/prtools for more.

Contents

prwaitbar off                % waitbar not needed here
delfigs                      % delete existing figures
randreset(1);                % takes care of reproducability

Define a classifier

u = knnc([],3);              % the untrained 3-NN classifier

Define datasets for training and testing

a = gendatb([20 20],2);      % define dataset
a = setlablist(a,[' A ';' B ']); % define class names
[t,s] = gendat(a,0.5);       % split it 50-50 in train set and test set
t = setname(t,'Train Set');  % name the train set
s = setname(s,'Test Set');   % name the test set

   Welcome to PRTools5
   For more information click <a href="http://37steps.com/prtools">here</a>.
 

Train the classifier

w = t*u;                     % train the classifier

Show the trained classifier on the training set

figure;
scatterd(t);                 % show training set
axis equal
plotc(w);                    % plot classifier
V = axis;

dt = t*w;                    % apply classifier to the training set
et = dt*testc;               % compute its classification error
fprintf('The apparent error: %4.2f \n',et); % print it
labt = getlabels(t);         % true labels of training set
labtc= dt*labeld;            % estimated labels of classified training set
disp([labt labtc]);          % show them. They correspond to the estimated error

The apparent error: 0.05 
 A  A 
 A  B 
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 B  B 
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Compute the apparent error and show the estimated and true labels in classifying the training set. They corespond to the apparent error and the classifier in the scatter plot

Show the trained classifier on the test set

figure;
scatterd(s);                 % show test set
axis(V);
plotc(w);                    % plot classifier

ds = s*w;                    % apply classifier on the test set
es = ds*testc;               % compute its classification error
fprintf('The test error: %4.2f \n',es); % print it
labs = getlabels(t);         % true labels of test set
labsc= ds*labeld;            % estimated labels of classified test set
disp([labs labsc]);          % show them. They correspond to the estimated error

The test error: 0.20 
 A  A 
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 A  B 
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 B  B 
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Compute the test error and show the estimated and true labels in classifying the test set. They corespond to the test error and the classifier in the scatter plot

Published with MATLAB® R2019a