Clear Filters
Clear Filters

correct and incorrect predictors

3 views (last 30 days)
Saeed Magsi
Saeed Magsi on 17 Sep 2022
Answered: Dinesh on 8 Jun 2023
Dear community members, i am stuck in a problem. I tried to search for the solution but i could not find. I want to get the input data (predictors) of an incorrect predicted class. How can i do that? i am just able to get the incorrect classes in the confusion chart but i need to find the input data of them.
for example lets take this example:
load satdata;
pt = cvpartition(satClass,'holdout',0.3);
predTrain = satData(training(pt),:);
classTrain = satClass(training(pt));
predValid = satData(test(pt),:);
classValid = satClass(test(pt));
knnClassifier = fitcknn(predTrain,classTrain,'Numneighbors',5);
yPred = predict(knnClassifier,predValid);
[c,lbls] = confusionmat(yPred,classValid);
Here i can only see the classes in yPred but i cant see the input data (predictors) of those classes. I hope i am clear to my question.
  3 Comments
Saeed Magsi
Saeed Magsi on 17 Sep 2022
load fisheriris
pt = cvpartition(species,'holdout',0.3);
predTrain = meas(training(pt),:);
classTrain = species(training(pt));
predValid = meas(test(pt),:);
classValid = species(test(pt));
knnClassifier = fitcknn(predTrain,classTrain,'Numneighbors',5);
yPred = predict(knnClassifier,predValid);
[c,lbls] = confusionmat(yPred,classValid);
Sorry for uploading the wrong code. I have now written a matlab documentation example code for knn classifier.
Saeed Magsi
Saeed Magsi on 17 Sep 2022
Edited: Saeed Magsi on 17 Sep 2022
I have now attached the excel files for yPred and classTrain. If you see in row 23 of yPred it has incorrect prediction of "virginica". Now i want to see its input data (predictors) which i dont know how can this be done? I need your help in this. Thank you once again.

Sign in to comment.

Accepted Answer

Dinesh
Dinesh on 8 Jun 2023
Hi Saeed!
One simple way to find the incorrect prediction is to iterate over the predictions and store the indices that are not equal in yPred and classValid. Using that indices array we can get the input data predictors for which the predictions were wrong.
indexes = [];
for i = 1:length(yPred)
% Compare values and store index if they don't match
if ~strcmp(yPred{i}, classValid{i})
indexes(end+1) = i;
end
end
data = predValid(indexes, :);
disp(data);
You can do this without for loop also. Use the 'cellfun' fuction
res = cellfun(@isequal, yPred, classValid);
% Find the indexes of non-matching elements
indexes = find(~res);
indexes
data = predValid(indexes, :);
disp(data);
Please refer to the following MATLAB documentation for more details and examples to use cellfun
Hope this helps!
Thank you.

More Answers (0)

Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!