The right thing to do would probably be to write a scikit-learn-contrib
package for them and see if they gather traction. If they perform well on
eg kaggle competitions, we know that we need them in :).
Cheers,
Gaël
On Fri, Jul 21, 2017 at 07:09:03PM -0400, Sebastian Raschka wrote:
> Maybe becau
Maybe because they are genetic algorithms, which are -- for some reason -- not
very popular in the ML field in general :P. (People in bioinformatics seem to
use them a lot though.). Also, the name "Learning Classifier Systems" is also a
bit weird I'd must say: I remember that when Ryan introduce
+1
LCS and its many many variants seem very practical and adaptable. I'm
not sure why they haven't gotten traction.
Overshadowed by GBM & random forests?
On Fri, Jul 21, 2017 at 11:52 AM, Sebastian Raschka
wrote:
> Just to throw some additional ideas in here. Based on a conversation with a
> co
Sounds good, Sebastian.
Thank you!
Raga
On Fri, Jul 21, 2017 at 2:52 PM, Sebastian Raschka
wrote:
> Just to throw some additional ideas in here. Based on a conversation with
> a colleague some time ago, I think learning classifier systems (
> https://en.wikipedia.org/wiki/Learning_classifier_sy
> Traditionally tree based methods are very good when it comes to categorical
> variables and can handle them appropriately. There is a current WIP PR to add
> this support to sklearn.
I think it's also important to distinguish between nominal and ordinal; it can
make a huge difference imho. I.
Just to throw some additional ideas in here. Based on a conversation with a
colleague some time ago, I think learning classifier systems
(https://en.wikipedia.org/wiki/Learning_classifier_system) are particularly
useful when working with large, sparse binary vectors (like from a one-hot
encodin
Thank you, Jacob. Appreciate it.
Regarding 'perform better', I was referring to better accuracy, precision,
recall, F1 score, etc.
Thanks,
Raga
On Fri, Jul 21, 2017 at 2:27 PM, Jacob Schreiber
wrote:
> Traditionally tree based methods are very good when it comes to
> categorical variables and
Traditionally tree based methods are very good when it comes to categorical
variables and can handle them appropriately. There is a current WIP PR to
add this support to sklearn. I'm not exactly sure what you mean that
"perform better" though. Estimators that ignore the categorical aspect of
these
Traditionally tree based methods are very good when it comes to categorical
variables and can handle them appropriately. There is a current WIP PR to
add this support to sklearn. I'm not exactly sure what you mean that
"perform better" though. Estimators that ignore the categorical aspect of
these
Hello,
I am wondering if there are some classifiers that perform better for
datasets with categorical features (converted into sparse input matrix with
pd.get_dummies())? The data for the categorical features are nominal (order
doesn't matter, e.g. country, occupation, etc).
If you could provide
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