Training and testing the data

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nkumar
nkumar el 7 de En. de 2013
Comentada: Son Dong el 13 de Oct. de 2020
I got a code from net for SVM classifier
clc
clear all
load fisheriris %# load iris dataset
groups = ismember(species,'setosa'); %# create a two-class problem
%# number of cross-validation folds:
%# If you have 50 samples, divide them into 10 groups of 5 samples each,
%# then train with 9 groups (45 samples) and test with 1 group (5 samples).
%# This is repeated ten times, with each group used exactly once as a test set.
%# Finally the 10 results from the folds are averaged to produce a single
%# performance estimation.
k=10;
cvFolds = crossvalind('Kfold', groups, k); %# get indices of 10-fold CV
cp = classperf(groups); %# init performance tracker
for i = 1:k %# for each fold
testIdx = (cvFolds == i); %# get indices of test instances
trainIdx = ~testIdx; %# get indices training instances
%# train an SVM model over training instances
svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ...
'Autoscale',true, 'Showplot',false, 'Method','QP', ...
'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1);
%# test using test instances
pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false);
%# evaluate and update performance object
cp = classperf(cp, pred, testIdx);
end
%# get accuracy
cp.CorrectRate
%# get confusion matrix
%# columns:actual, rows:predicted, last-row: unclassified instances
cp.CountingMatrix
In comment
%If you have 50 samples, divide them into 10 groups of 5 samples each,
%# then train with 9 groups (45 samples) and test with 1 group (5 samples).
I have 5x6 samples
have divided 5x4 to training and 5x2 for testing
now please tell how to process above mentioned step

Respuestas (2)

liu xiaolong
liu xiaolong el 13 de Mzo. de 2013
Hello!Can you give me the code of the function svmtrain? Thank you very much.

noo no
noo no el 17 de Oct. de 2015
i have code in matlab how trining and testing this code?
  1 comentario
Son Dong
Son Dong el 13 de Oct. de 2020
Can you give me please. Thank you so much.

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