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MARS Modeling on the Python Data Stack
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Author(s):
Jason Rudy
Location
A1
Date
mar Wed 20
Days Raw Files
Start
15:10
First Raw Start
14:33
Duration
00:50:00
Offset
0:36:22
End
16:00
Last Raw End
16:30
Chapters
00:00
0:28:07
Total cuts_time
37 min.
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Multivariate Adaptive Regression Splines (MARS, also known as earth) is a non- parametric regression method originally published by Jerome Friedman in 1991. It is particularly useful for modeling medium to high dimensional systems where it is not known ahead of time which variables are predictive or in what form, but it is suspected that an additive model with perhaps a few low order interactions is appropriate. An implementations is available for R, and MARS is included in the Orange data mining system for Python. However, there is currently no easy way to integrate MARS with a Pandas / Numpy based data analysis pipeline. In my talk, I'll be explaining the MARS algorithm and demonstrating a new Pandas / Numpy compatible Python implementation with a few example problems.
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