MATLAB: Using perfcurve with neural network

classificationneural network

As a matter of fact, I want to use neural network in order to do classification (patternnet). Unfortunately, I have some problems in applying the "perfcurve" function because I do not know how to extract "scores". Therefore, I would be appreciated if you could let me know how I should get the "scores".

[Xpr,Ypr,Tpr,AUCpr] = perfcurve(targets, scores);

regards,

Best Answer

Currently, I cannot help because I have never encountered this function. When I get time I will consult the documentation using the commands

help perfcurve
doc  perfcurve
type perfcurve

AND

 search both the NEWSGROUP and ANSWERS for relevant posts.

Meanwhile, you should do the same. I suspect that you will find your answer before I can.

Hope this helps.

Greg

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MATLAB: How to write a Code for regularization of RBF Neural Network

Writing your own code will not be easy. Why don't you search the net? Especially search comp.ai.neural-nets. I know there are references for good RBF codes there.

Why do you need an RBF?

Why do you need a regularized RBF?

Why not an MLP or regularized MLP?

Is this regression or classification?

How large are your input and target matrices?

Hope this helps.

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MATLAB: Precision/Recall (perfcurve)

perfcurve() is meant for computing performance curves for your model - such as PR curves or ROC curves. In the literature, you may find these curves used for a more rigorous examination of a model's performance than can be given by a single score.

I recommend looking up the doc linked below. The Algorithms > Pointwise Confidence Bounds section explains what goes on under-the-hood after you call the function with your particular arguments. It samples from your input data multiple times, with different thresholds, to come up with a range of P/R scores, which are used to plot the curve.

perfcurve() doc: https://www.mathworks.com/help/stats/perfcurve.html

To calculate a single value, you may extract counts of TP, FP, TN & FN from your labels and predictions; then manually compute -

Precision = TP / (TP + FP)

Recall = TP / (TP + FN)

Should be done in 2 lines of code.

Alternatively, you may use confmat() to produce a confusion matrix, to readily give you TP/FP/TN/FN counts. And then do the same computation.

%Using this method, here's an anonymous function to calculate precision.
% << for an existing Confision Matrix 'ConfusionMat' >>
precision = @(confusionMat) diag(confusionMat)./sum(confusionMat,2);
% And another for recall
recall = @(confusionMat) diag(confusionMat)./sum(confusionMat,1)';
% Computes a vector of P/R values - each value corresponds to selecting a different label as +ve.

Hope it Helps!

Best Answer

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