"Insufficient results with M5P regression tree"

michaelhecht

Hello,

if anyone is interested please try the following:

produce a file containing two columns

x = 0, 0.1, 0.2, ..., 12.6;
y = sin(x)

Then apply M5P (with or without normalization).

The result is quite disappointing. Does anyone know how to get an acceptable result?
I expected to get something like a picewise linear approximation of the sin function,
but got something far away from this.

Thank You.

keith

michaelhecht wrote:
Hello, if anyone is interested please try the following:

It would actually be more helpful (and more likely to generate a response), if you include the XML for the process you are running in your forum post.

produce a file containing two columns x = 0, 0.1, 0.2, ..., 12.6; y = sin(x) Then apply M5P (with or without normalization). The result is quite disappointing. Does anyone know how to get an acceptable result? I expected to get something like a picewise linear approximation of the sin function, but got something far away from this.

There's a fair amount of ambiguity in your question.  What constitutes an acceptable result?  Is there a reason you believe this M5P is a good learner in this situation?  Did you experiment with any the options for the M5P learner? 

I just tried the following process, and the only changes from the default settings are to click the check box for parameters N, U, and R :

<operator name="Root" class="Process" expanded="yes">
    <operator name="CSVExampleSource" class="CSVExampleSource">
        <parameter key="filename"	value="c:\temp\xy.csv"/>
        <parameter key="label_name"	value="y"/>
    </operator>
    <operator name="W-M5P" class="W-M5P">
        <parameter key="keep_example_set"	value="true"/>
        <parameter key="N"	value="true"/>
        <parameter key="U"	value="true"/>
        <parameter key="R"	value="true"/>
    </operator>
    <operator name="ModelApplier" class="ModelApplier">
        <list key="application_parameters">
        </list>
    </operator>
</operator>

And the plot of x vs. prediction(y) looks, to my eyes, much more sin-like.  But I don't know if using an unpruned, unsmoothed learner makes sense for your problem.

Keith