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Raphael
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Gilles 'SO- stop being evil'
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Pattern classification issues and plots: what goes into the sample?

I am trying to compare the performance of a classification result with Bayes classifier, K-NN, Piece wise component analysis (PCA). I have doubts regarding the following (please excuse my lack of programming skills since I am a biologist and not a programmer thus finding the matlabMatlab documentation hard to follow  ).

  1. In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
    Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

What goes in the sample because my data is a 100 row 1 column for each of the classes? So Group 1 contains data like $[0.9;0.1;......n]$ where $n=100$. Would the sample be a vector containing random mixtures of the data points from the three classes? Same query/doubtquestion for Trainingthe Training matrix.

  1. I cannot follow crossval() & cvpartition() functions given in Matlab documentation crossval(). Would be obliged if a simpler version of it is provided here.

  2. Is there a code for multi class ROC since internet resources provide information about binary class ROC. How to proceed for ROC plot when there are 3 or more classes like in my case.

  3. Is it possible to obtain a surface plot from the confusion mmatrix from the command confusemat()

Pattern classification issues and plots

I am trying to compare the performance of a classification result with Bayes classifier, K-NN, Piece wise component analysis (PCA). I have doubts regarding the following (please excuse my lack of programming skills since I am a biologist and not a programmer thus finding the matlab documentation hard to follow  )

  1. In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

What goes in the sample because my data is a 100 row 1 column for each of the classes? So Group 1 contains data like $[0.9;0.1;......n]$ where $n=100$. Would the sample be a vector containing random mixtures of the data points from the three classes? Same query/doubt for Training matrix.

  1. I cannot follow crossval() & cvpartition() functions given in Matlab documentation crossval(). Would be obliged if a simpler version of it is provided here.

  2. Is there a code for multi class ROC since internet resources provide information about binary class ROC. How to proceed for ROC plot when there are 3 or more classes like in my case.

  3. Is it possible to obtain a surface plot from the confusion mmatrix from the command confusemat()

Pattern classification: what goes into the sample?

I am trying to compare the performance of a classification result with Bayes classifier, K-NN, Piece wise component analysis (PCA). I have doubts regarding the following (please excuse my lack of programming skills since I am a biologist and not a programmer thus finding the Matlab documentation hard to follow).

In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
    Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

What goes in the sample because my data is a 100 row 1 column for each of the classes? So Group 1 contains data like $[0.9;0.1;......n]$ where $n=100$. Would the sample be a vector containing random mixtures of the data points from the three classes? Same question for the Training matrix.

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Ran G.
Ran G.
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I am trying to compare the performance of a classification result with Bayes classifier, K-NN, Piece wise component analysis (PCA). I have doubts regarding the following (please excuse my lack of programming skills since I am a biologist and not a programmer thus finding hard to follow the matlab documentation hard to follow )

  1. In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

What goes in the sample because my data is a 100 row 1 column for each of the classes? So Group 1 contains data like $[0.9;0.1;......n]$ where $n=100$. Would the sample be a vector containing random mixtures of the data points from the three classes? Same query/doubt for Training matrix.

  1. I cannot follow crossval() & cvpartition() functions given in Matlab documentation crossval(). Would be obliged if a simpler version of it is provided here.

     

  2. cvpartition()Is there a code for multi class ROC since internet resources provide information about binary class ROC. How to proceed for ROC plot when there are 3 or more classes like in my case.

  3. Is it possible to obtain a surface plot from the confusion mmatrix from the command confusemat()

function given in Matlab documentation crossval(). Would be obliged if a simpler version of it is provided here.

  1. Is there a code for multi class ROC since internet resources rovide information about binary class ROC. How to proceed for ROC plot when there are 3 or more classes like in my case.

  2. Is it possible to obtain a surface plot from the confusion mmatrix from the command

    confusemat()

I am trying to compare the performance of a classification result with Bayes classifier, K-NN, Piece wise component analysis (PCA). I have doubts regarding the following (please excuse my lack of programming skills since I am a biologist and not a programmer thus finding hard to follow the matlab documentation)

  1. In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

What goes in the sample because my data is a 100 row 1 column for each of the classes? So Group 1 contains data like $[0.9;0.1;......n]$ where $n=100$. Would the sample be a vector containing random mixtures of the data points from the three classes? Same query/doubt for Training matrix.

  1. I cannot follow crossval() &

    cvpartition()

function given in Matlab documentation crossval(). Would be obliged if a simpler version of it is provided here.

  1. Is there a code for multi class ROC since internet resources rovide information about binary class ROC. How to proceed for ROC plot when there are 3 or more classes like in my case.

  2. Is it possible to obtain a surface plot from the confusion mmatrix from the command

    confusemat()

I am trying to compare the performance of a classification result with Bayes classifier, K-NN, Piece wise component analysis (PCA). I have doubts regarding the following (please excuse my lack of programming skills since I am a biologist and not a programmer thus finding the matlab documentation hard to follow )

  1. In the Matlab code

    Class = knnclassify(Sample, Training, Group, k)
Group =  [1;2;3]   //where 1,2,3 represents Class A,B,C respectively.

What goes in the sample because my data is a 100 row 1 column for each of the classes? So Group 1 contains data like $[0.9;0.1;......n]$ where $n=100$. Would the sample be a vector containing random mixtures of the data points from the three classes? Same query/doubt for Training matrix.

  1. I cannot follow crossval() & cvpartition() functions given in Matlab documentation crossval(). Would be obliged if a simpler version of it is provided here.

     

  2. Is there a code for multi class ROC since internet resources provide information about binary class ROC. How to proceed for ROC plot when there are 3 or more classes like in my case.

  3. Is it possible to obtain a surface plot from the confusion mmatrix from the command confusemat()

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user1214586
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