Find a fixed accuracy using confusion matrix.

I used a confusion matrix to find accuracy. I am getting different accuracy in each run. How can get a fixed accuracy? Anybody, please help me.

clc;
clear;
clc;
clear;
data=readtable('data2.xlsx', 'ReadVariableNames', false);
  data.Var1 = findgroups(data.Var1); % convert column 
 data.Var9 = findgroups(data.Var9); % convert column 
minpts=3;
epsilon=30;

data = table2array(data);
[idx, corepts] = dbscan(data,epsilon,minpts);

fig1 = figure();
gscatter(data(:,1),data(:,2),idx);

fig2 = figure();
ax = axes();
hold on;

core=data(corepts, :);
core_idx = idx(corepts, :);
gscatter(core(:,1),core(:,2),core_idx);

centers = splitapply(@(x) mean(x, 1), core, core_idx);
gscatter(centers(:,1), centers(:,2), 1:size(centers,1));

   dist2 = (data(:,1) - centers(:,1).').^2 + (data(:,2) - centers(:,2).').^2;
          [~,id] = mink(dist2,336,1);
          clusters = data(id);
    
          maximum_num_clusters = 7;
    
  Z = linkage(clusters, 'average');
id= cluster(Z, 'Maxclust', maximum_num_clusters);
       k = 3;
  [idx1,V,D] = spectralcluster(Z,k);

           I=data(1:335,9);
   
[m,order] = confusionmat(I,idx1);

figure
cm = confusionchart(m,order);
c = 3;

TP = cm.NormalizedValues(c,c) ;          % true class is c and predicted as c
FP = sum(cm.NormalizedValues(:,c))-TP ;  % predicted as c, true class is not c
FN = sum(cm.NormalizedValues(c,:))-TP ;  % true class is c, not predicted as c
TN = sum(diag(cm.NormalizedValues))-TP;  % true class is not c, not predicted as c
      A=(TP+TN)/(TP+TN+FP+FN)*100

Answer 1

[Aditya Patil]

You can set the random seed to get predictable results, as follows

rng(1234);

Check the rng documentation.