On Wed, Nov 07, 2012 at 05:23:50PM -0800, Doug Coleman wrote:
> Is anyone planning on attending this conference?
I am not going this time. Sorry.
> Any of the tutorials/talks/ workshops look especially interesting?
NIPS is usually very interesting and of high quality.
Gaël
The biglearn workshop looks killer.
On Wed, Nov 7, 2012 at 5:23 PM, Doug Coleman wrote:
> Hello,
>
> Is anyone planning on attending this conference? Any of the
> tutorials/talks/workshops look especially interesting?
>
> http://nips.cc/Conferences/2012/Program/
>
> I'm debating whether to go or
Hello,
Is anyone planning on attending this conference? Any of the
tutorials/talks/workshops look especially interesting?
http://nips.cc/Conferences/2012/Program/
I'm debating whether to go or not and which sessions to attend because it's
a 40 minute drive from where I live. If anyone is going,
>
> Indeed Abhi which section specific section of the documentation (or
> docstring) led you to ask this question?
>
> The note on this page is pretty explicit:
>
> http://scikit-learn.org/dev/modules/multiclass.html
>
> Along with the docstring:
>
> http://scikit-
learn.org/dev/modules/gener
Am 07.11.2012 15:48, schrieb paul.czodrow...@merckgroup.com:
> However, f1_score is not found. I would have suspected that this works in
> analogy to the recall_score.
How do you mean it is not found? It is in sklearn.metrics.
>> If you want your confusion matrix to be more balanced, you can try tw
Dear Andreas,
Dear Gilles,
Dear SciKitters,
> Hi Paul
> Tuning min_samples_split is a good idea but not related to imbalanced
> classes.
> First, you should specify what you want to optimize. Accuracy is usually
> not a good measure for imbalanced classes. Maybe F-score?
How would one do that?
I j
Hi Paul
Tuning min_samples_split is a good idea but not related to imbalanced
classes.
First, you should specify what you want to optimize. Accuracy is usually
not a good measure for imbalanced classes. Maybe F-score?
If you want your confusion matrix to be more balanced, you can try two
things (
Hello Paul,
> Do fully developed trees make sense for rather small datasets? Overall, I
> have 622 samples with 177 features each. Isn't there the risk of
> overfitting?
Yes, overfitting might happen, but it should be limited since you are
building randomized trees and average them together.
>
>
Dear SciKitters,
given a dataset of 622 samples and 177 features each, I want to classify
those given an experimental classification stating "0" or "1".
After splitting up into training and test set, I trained a RandomForest the
following way:
"
from sklearn.ensemble import RandomForestClassifie
