> Could you clarify the following on prediction errors.
>
> Which error i should take for comparing the algorithms.
>
> Last time you clarified that correlation coefficient cannot a parameter for
> selecting the best predicting algorithm.
>
> Now if you compare the MLP vs RF MLP has low Absolute errors where as RF has
> lower squared errors.
>
> Pl clarify which algorithm i should consider for my numeric prediction.
>
> Description
>
> MLP -4H
>
> RF
>
> Correlation coefficient
>
> 0.247
>
> 0.535
>
> Mean absolute error
>
> 3.692
>
> 3.742
>
> Root mean squared error
>
> 4.915
>
> 4.6
>
> Relative absolute error
>
> 57.59 %
>
> 58.37%
>
> Root relative squared error
>
> 72.67 %
>
> 68.10%
To be honest, both models don't seem to perform very well. Correlation
coefficient (CC) should be close to 1 (somewhere above 0.95) and root
relative squared error (RRSE) should be close to 0% (definitely below
10%). But then I don't know what data your dealing.
If I had to choose from those two models, I'd go for the RF one, as
the CC is higher and RRSE is lower. But that's just my 2c.
Cheers, Peter
--
Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
http://www.cs.waikato.ac.nz/~fracpete/ Ph. +64 (7) 858-5174
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