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Add more classifiers per #39 #40

Merged
merged 4 commits into from
Dec 4, 2015
Merged

Add more classifiers per #39 #40

merged 4 commits into from
Dec 4, 2015

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rhiever
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@rhiever rhiever commented Dec 4, 2015

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TPOT now includes logistic regression, SVM, and kNN classifiers.
Some of the docstrings were incomplete, and some of the docstrings were
wrong. Many of them have now been fixed.
rhiever pushed a commit that referenced this pull request Dec 4, 2015
@rhiever rhiever merged commit f4df1fb into master Dec 4, 2015
@rhiever rhiever deleted the more-classifiers branch December 4, 2015 18:43
@bartleyn
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bartleyn commented Dec 4, 2015

training_features = input_df.loc[input_df['group'] == 'training'].drop(['class', 'group', 'guess'], axis=1).values

I've always wondered -- is it faster to drop columns or to create a slice all the other columns?

@rhiever
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rhiever commented Dec 4, 2015

I've never checked to be honest. My assumption has always been that dropping a few specific columns is faster than finding out the column names of all the other columns and slicing down to those.

@bartleyn
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bartleyn commented Dec 4, 2015

I agree with your intuition -- out of curiosity I'll take some time later tonight and see if there are any existing empirical test results pointing one way or the other, otherwise I'll run my own tests and see.

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2 participants