How to prepare the data for Classification learner Toolbox?
3 vues (au cours des 30 derniers jours)
I am doing parking lot detection by using Matlab tool called classification learner. I prepared the dataset as tables and imported it to the classifier but unfortunately, I got training model with 75% accuracy.
I do not know how to improve the classifier accuracy or and how to get rid of variables overlap in scatter plot?
is the problem with my data or in the way I prepared it ?
here is the code for Image Batch processor tool:
function occupied = myimfcn(im)
J=imresize(im, [30 80]);
B = reshape(J,1,);
preparing data code. allresults(1,i).occupied The struct array I got from Image Batch processor tool
B = repmat('occupied',500,1);
[sA1, sA2] = size(A);
[sB1, sB2] = size(B);
L(sA1+1:sA1+sB1, 1:sB2) = B;
L(1:sA1, 1:sA2) = A;
D = array2table(C);
Santhosh A V le 14 Mar 2019
Data preparation for training seems good. You can refer to “Compare and Improve Classification Models” section in below link to improve the accuracy of the classifier. https://in.mathworks.com/help/stats/train-classification-models-in-classification-learner-app.html#bu3xf0x-1
Hope this helps.