Hi all,
I am looking into how to combine classifiers using Scikit-learn.
I think for general purpose, it could be useful to have functions like
stacking and voting in scikit-learn. Is there any plan of developing
ensemble methods?
For now, I am writting my own snippet for stacking. First phase would be
stacking simply on predictions from different models and next would be
stacking on probabilities.
However, while dealing with the predictions, I get a problem of classifying
nominals:
In details, in level 0, several (say m) classifiers are used, and m
predictions for each sample are gathered to form a Z matrix.
In m=7, Z could look like:
[ [1 1 2 1 1 1 1]
[3 3 3 6 3 3 3]
....
[3 9 3 2 3 3 3]
]
y in this case, could be:
[1 3 ... 3 ]
So, on level 1 (stacking level), a new classifier's task is to predict base
on the results from level 0, e.g., for a test case, level 0 generates:
[1 6 6 6 6 6]
we expect level 1 classifier to give prediction as 6.
Because in stakcing, level 1 is a machine learning classifier rather than
selecting mode, one excepts stacking will out-perform voting in general.
The problem is that all the numbers in Z are predication of categories,
these numbers are nomial without any real quantitative meaning.
I directly applied classification methods on (Z,y), results are terrible,
except for tree classifier.
Also regressions with rouding are tried, results are relatively higher than
classification, but not as high as level 0. But still, regression on nomial
numbers does not seem to make too much sense to me.
I though about normalization and scaling in preprocessing, but not sure if
they are relevant here.
I wonder what is the right way to classify based on nomials?
Thanks a lot!
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