in the
right range)
Ram
On Tue, May 19, 2015 at 5:45 AM, Keerthi
wrote:
> Hi ,
>
> can you pls share how you resolved the parsing issue. It would be of great
> help...
>
> Thanks.
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list
Hi ,
can you pls share how you resolved the parsing issue. It would be of great
help...
Thanks.
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Decision-tree-categorical-variables-tp12433p22943.html
Sent from the Apache Spark User List mailing list
Was able to resolve the parsing issue. Thanks!
From: ssti...@live.com
To: user@spark.apache.org
Subject: FW: Decision tree: categorical variables
Date: Wed, 20 Aug 2014 12:48:10 -0700
From: ssti...@live.com
To: men...@gmail.com
Subject: RE: Decision tree: categorical variables
Date: Wed, 20
From: ssti...@live.com
To: men...@gmail.com
Subject: RE: Decision tree: categorical variables
Date: Wed, 20 Aug 2014 12:09:52 -0700
Hi Xiangrui,
My data is in the following format:
0,1,5,A,8,1,M0,1,5,B,4,1,M1,0,2,B,7,0,U0,1,3,C,8,0,M0,0,5,C,1,0,M1,1,5,C,8,0,U0,0,5,B,8,0,M1,0,3,B,2,1,M0,1,5,B
The categorical features must be encoded into indices starting from 0:
0, 1, ..., numCategories - 1. Then you can provide the
categoricalFeatureInfo map to specify which columns contain
categorical features and the number of categories in each. Joseph is
updating the user guide. But if you want to
Hi All,
Is there any example of MLlib decision tree handling categorical variables? My
dataset includes few categorical variables (20 out of 100 features) so was
interested in knowing how I can use the current version of decision tree
implementation to handle this situation? I looked at the Labe
