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Tuning Machine Learning Parameters using scikit-learn Gridsearch
--client
depy
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depy_2015
--room room_LL104 9762 --force
Next: 11 Speeding Up Python Data Analysis Using Cython
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Marks
Author(s):
Kevin Goetsch
Location
Room LL104
Date
may Fri 29
Days Raw Files
Start
10:00
First Raw Start
09:59
Duration
00:40:00
Offset
0:00:44
End
10:40
Last Raw End
10:41
Chapters
00:00
Total cuts_time
41 min.
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At Braintree we rely on scikit-learn's Pipeline and Gridsearch concepts to build and tune our predictive models. I'll talk through how this looks, the benefits, and the custom classes we've built out to augment this process to integrate Pandas dataframes.
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2015-05-29/09_59_16.dv
Apply:
09:59:16 - 10:41:02 ( 00:41:46 )
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00:41:46
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09:59:16
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10:41:02
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