how to evaluate my result knn code using confusion matrix

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merlin toche 2023-1-9

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评论： merlin toche 2023-1-30

Untitled5.m

sir, although I can't replace the old data, I have tried several times to use the App toolbox (ClassificactionLearner) to evaluate the confusion matrix using the old data but I always fail a month ago already. can you help me in this direction through a code to determine this confusion matrix in order to know the well classified data and the badly classified data? thank you for your continued support

attached my code

thank you!!

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Rajeev 2023-1-16

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编辑：Rajeev 2023-1-16

There are two requirements for the "confusionmat" function that are not being fullfilled in your matlab script:

The type of the input must be vectors or character matrices. Your inputs are of the type double. You can use the "num2str" function to convert the double type to char array. For example

char_test_Coords = num2str(test_Coords)

The size of both the character array inputs should be the same. In your script, the length of train_Coords is 120 and the length of test_Coords is 30.

Making the above changes should fix the problem.

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Rajeev 2023-1-17

No worries.

As you have mentioned, the data has been partitioned into training(80%) and testing(20%) sets.

Ideally, the next step is to train you model on the training data. Once trained, you test your model using the rest of the data i.e. the test data (20%).

Let us take an example to understand it better, I have taken this image from the website https://builtin.com/data-science/train-test-split.

For this example, I will use the variables names as given in the image below. The steps one should follow are:

Train your model using the data 'X_train' and 'y_train'. In your case, the (X_train, y_train) is 80% of the total data.
Predict the results of 'X_test' using the model trained in step 1. Store the results in 'y_test_pred'.
Pass 'y_test_pred' and 'y_test' in the confusionmat function. In this case, both 'y_test_pred' and 'y_test 'are of the same dimension.

The results that you obtain by running your model on the test data and the original result that you already have in your test data are given as inputs to the 'confusionmat' function.

NOTE: confusionmat works only for classification problems.

Rajeev 2023-1-17

编辑：Rajeev 2023-1-18

Hi, I went through your code and I think there are some pieces of code that needs modification. Here are my suggestions:

You are trying to sort the array based on distances. Correct me if I am wrong, are you looking for the top n elements with the least distance along with their classes? If so, then you can replace these lines in your code

distanceofIndex=[];

temp=0;

gemp=0;

for i=1:length(distanceofIndex)

for j=1:(length(distanceofIndex)-i)

if(distanceofIndex(j)>distanceofIndex(j+1))

temp=distanceofIndex(j);

distanceofIndex(j)=distanceofIndex(j+1);

distance(j+1)=temp;

gemp=trainClass(j);

trainClass(j)=trainClass(j+1);

trainClass(j+1)=gemp;

end

%4.take first k element from the c array now

k=5;

classy=[];

for i=1:k

classy=[classy trainClass(i)];

end

with

distanceofIndex = distancesOfTheIndexes(:,1);

pred_classes = trainClass(indexes(:,1));

dist_class_aug_matrix = [distanceofIndex, pred_classes'];

dist_class_aug_matrix = sortrows(dist_class_aug_matrix);

% to get the top n elements from the sorted c array

n = 7 % for example

classy = dist_class_aug_matrix(1:n,2)

Now for the part where you are trying to calculate the confusion matrix, you should feed the classes to the confusionmat function instead of the coordinates. Refer to the documentation for more Compute confusion matrix for classification problem - MATLAB confusionmat (mathworks.com).

But it seems like you do not have the classes for the test_Coords data. The size of the trainClass vector is 120 instead of 150. If you had the classes for train_Coords as well, then you could simply pass these classes with the prediceted one (pred_classes) to get the desired confusion matrix.

Rajeev 2023-1-25

编辑：Rajeev 2023-1-25

在 MATLAB Online 中打开

myknnessay.m

I am not getting the error when I ran your script. Although, I did get the plot that you have attached in the comment. This plot is incorrect as

plot(x_train,'b.','linewidth',2,'MarkerSize',10)

plots all the coordinates against [1:length(x_train)]. To plot the coordinates of x_train, you need to give the data for x axis and y axis separately like:

plot(x_train(:,1),x_train(:,2),'b.')

Here is the output of test data and train data. As indicated in the figure, the test data belongs to each class of the data. The confustion chart shows that all the predictions made by 'knnsearch' are correct.

Fig 1: The original dataset as per your constraints.

Fig 2: Visual representation of split of test and train data (i.e. x_train and x_test).

Fig 3: The confusion matrix for the faults predicted by the function knnsearch.

I have also attached the file that can plot the results as indicative in the screenshots. Since, confusionchart doesn't work for you, you can simply use the confusionmat and view it in the command line window to get the matrix.

Rajeev 2023-1-30

plot_dataset.m

Upon plotting and observing your dataset, it can be seen that the datapoints that belongs to the same class are not close to each other. It is necessary for them to be in clusters for knn search algorithm to work.

Regardless, if the dataset is passed to the knnsearch function, the results are:

As, you can see, the confusion matrix now shows a lot of error/misclassification as the data is not clustered well.

I have also attached the code for your reference.

merlin toche 2023-1-30

What will I say again beyond thank you, sir @Rajeev receive all my gratitude for your competence and your pedagogy in the transmission of knowledge to us who needed. you have a big heart! unfortunately for me the version R2015a I have fails to read the code you sent. however while waiting to change my R2015a version, can I continue if something still bothers me to turn to you and your team?

a concern please: is it necessary to use a threshold for fault detection with kNN? if yes how to do it?

thank you again and again for all you do for me

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