Hi,
On Aug 5, 2009, at 2:11 PM, Noah Silverman wrote:
Hello,
I asked this as part of a previous message, but never really figured
out a usable solution. So this is a second attempt.
I have an process containing an SVM. The end result is the
probability that the class is true. That result is added back to
the original data.
So I wind up with a data.frame that looks like this
label,v1,v2,v3,prob_true
What I want to do is measure how accurate my model is for each range
of probability. (I've seen this done is a few published papers and
found it a very useful way to visualize things.)
My hope/guess is that there is some kind of package for R that does
this since it should be a common need.
Here is an example of what I'd like to be able to generate:
range number of items mean(probability) true_accuracy
100-90% 20 .
924 .90
90-80% 50 .
825 .84
80-70% 214 .
75 .71
etc...
range is the range of predicted values by the SVM
mean(probability) is the mean of the PREDICTED probability of items
in that range
true_accuracy is the mean of the ACTUAL probability of items in that
range.
In English I would explain it as, "Of the data where our SVM
predicted a true probability of 70-80%, the data was actually 71%
true."
It might be really helpful to be able to graph this somehow.
(Again, There must be some package in R for this??)
With mean(predicted_probability) on one axis and
mean(true_probability) on the other axis.
Any thoughts, comments, ideas, etc. would be appreciated!
Take a look at the cut function, and the code in the examples of ?cut
(eg, take a look at the output when combined w/ table(cut(..)) ).
Sending in your own vector for the ``breaks`` param inorder to bin as
you like should get you 90% of the way to building the table you're
after.
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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