How to calculate confusion matrix , accuracy and precision

Hi

I have two logical tables 100 x 100 for each that contain 0 & 1 values . one table for original values and other table is for predicted values

i want to know how can i make confusion matrix and calculate accuracy and precision for predicted values in comparision to original values

Here the tables:-

original values

predicted values

Answer 1

[Srivardhan Gadila]

You can refer to the following functions available in MATLAB to compute confusion matrix: Functions for computing "confusion matrix".

And accuracy can be calculated as simple as follows:

accuracy = sum(OrigValues == PredValues,'all')/numel(PredValues)

Make sure that the above computations are performed properly w.r.t the number of samples dimension and necessary changes are to be made based on it (i.e., Dimension of number of samples can be number of rows or number of columns or the number of tables itself in your case as it is not mentioned anywhere in the question).

Answer 2

[Ayokunmi Opaniyi]

I will like to calculate the accuracy, precision and recall of my dataset in matlab.

can anyone please help me how to go about it with the sample code.

Thank you in advance.

[sed]

figure

cm=confusionchart(Ytest,YPred)

cm.ColumnSummary = 'column-normalized';

cm.RowSummary = 'row-normalized';

cm.Title = ' Confusion Matrix';

[m,order]=confusionmat(Ytest,YPred);

Diagonal=diag(m);

sum_rows=sum(m,2);

Precision=Diagonal./sum_rows;

Overall_Precision=mean(Precision)

sum_col=sum(m,1);

recall=Diagonal./sum_col';

overall_recall=mean(recall)

F1_Score=2*((Overall_Precision*overall_recall)/(Overall_Precision+overall_recall))