Compute the acuarcy or error of the output?

I have two vectors Y and Yprd, each one is 1x602 double. Y contains the real data which represent the class label either one or zero. Yprd contains the prediction of the data which real numbers. Here is an example Y=[0 1 1 1 0] Yprd=[0.456 0.986 -0.008 0.987 0.0002] I would like to compute the accuracy of the model (or error) when at Yprd vector any values greater than 0.5 can be one and less than can zero.

[John D'Errico]

What model? I don't see that you have posed any model at all here. Before you can talk about prediction error, you must have a model.

[Salem]

I used neural network model, I built it myself as I want some specific operations.

Answer 1

[Roger Stafford]

   Ymodel = 1*(Y>.5) + 0*(Y<=.5);  % The model from the predictions (right half unnecessary)
   p = sum(abs(Y-Ymodel))/size(Y,2); % Fractional error

[Greg Heath]

The usual convention for classifiers is to have c-dimensional {0,1} unit vectors for targets and nonnegative c-dimensional unit vectors for outputs

The relationship between the column vectors and the class indices are given by the functions

 IND2VEC and VEC2IND

see their help and documentation.

Hope this helps.

Greg