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| Author: |
Tim Frink |
| Subject: |
Parsing decision trees |
| Body: |
Hi,
I'm looking for a way to parse decision trees that very
generated with a machine learning tool into equivalent
C++ code.
Let's say I've learning data stored in a CSV file that looks
like this:
a b c label
1 3 5 0
0 5 2 1
....
The decision tree represents a classification that is used to steer
a particular routine within a C++ program. The column notations (a,b,c)
are real variables within the routine. 'a' is a boolean variable, while
'b' and 'c' are numeric (integer) variables. There are some tools which
can import this CSV file and automatically generate a decision tree. These
trees can be translated by hand into equivalent if-then-else C++
statements. However, this is very tedious for larger trees.
Moreover, I would like to use the leave-one-one cross-validation for which
I have to generate as many decision trees as many examples I have. This
cannot be done manually in an acceptable amount of time.
Do you know a tool that allows a generation of decision trees that can
be automatically translated int into C++ code like
if( a < 10 ) {
if( b > 20 )
return true;
else if( c < 100 )
return false;
}
Thank you for your help.
Regards,
Tim
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| Topic: |
Parsing decision trees |
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| Message: |
Author |
Date |
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*Message 1* |
Tim Frink |
Fri, 19 Sep 2008, 8:17 pm |
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