By removing label from the training set, and then rerun the process (fit,
predict, etc.). The result looks reasonable.
Thank you very much.
- Original Message -
From: Andreas Mueller
To: Jason Williams ;
scikit-learn-general@lists.sourceforge.net
Cc:
Sent: Thursday, 15 August 2013
tain header
3969
$ cat /tmp/partitioned_data/train_set | wc -l # contain header
12064
$ echo $((12063+3968))
16031
- Original Message -
From: Gilles Louppe
To: "scikit-learn-general@lists.sourceforge.net"
Cc: Jason Williams
Sent: Thursday, 15 August 2013, 3:49
S
I ran a few test based on Random Forest Classifier
(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
with default setting. The classification (repeated the classification
procedure several times) is nearly 100% correct. That seems to be overfitting.
olumns in your X array before feeding it to your estimator.
>
>
>(Note however that Random Forests have the advantages of being robust with
>respect to noise attributes. Training with or without shouldn't change the
>result by much.)
>
>
>Best,
>
>
>Gilles
>
simpler.
Thank for help
From: Roland Szabo
To: Jason Williams ;
scikit-learn-general@lists.sourceforge.net
Sent: Tuesday, 13 August 2013, 6:11
Subject: Re: [Scikit-learn-general] Can Random Forest Classifer ignore specific
fields?
Isn't it simpler to
I follow an example found on the internet
(http://blog.yhathq.com/posts/random-forests-in-python.html) for using Random
Forest Classifer. The result looks working. From the sample code, it looks like
taking all attributes to train the model. But checking api
(http://scikit-learn.org/stable/modu
