Running l1 and picking non zero coefficient s gives a good estimate of
interesting features as well...
On Jun 17, 2015 4:51 PM, Xiangrui Meng men...@gmail.com wrote:
We don't have it in MLlib. The closest would be the ChiSqSelector,
which works for categorical data. -Xiangrui
On Thu, Jun 11,
ChiSqSelector calls an RDD of labeled points, where the label is the
target. See
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala#L120
On Wed, Jun 17, 2015 at 10:22 PM, Ruslan Dautkhanov
dautkha...@gmail.com wrote:
Thank you
Got it. Thanks!
--
Ruslan Dautkhanov
On Thu, Jun 18, 2015 at 1:02 PM, Xiangrui Meng men...@gmail.com wrote:
ChiSqSelector calls an RDD of labeled points, where the label is the
target. See
We don't have it in MLlib. The closest would be the ChiSqSelector,
which works for categorical data. -Xiangrui
On Thu, Jun 11, 2015 at 4:33 PM, Ruslan Dautkhanov dautkha...@gmail.com wrote:
What would be closest equivalent in MLLib to Oracle Data Miner's Attribute
Importance mining function?
Thank you Xiangrui.
Oracle's attribute importance mining function have a target variable.
Attribute importance is a supervised function that ranks attributes
according to their significance in predicting a target.
MLlib's ChiSqSelector does not have a target variable.
--
Ruslan Dautkhanov
What would be closest equivalent in MLLib to Oracle Data Miner's Attribute
Importance mining function?
http://docs.oracle.com/cd/B28359_01/datamine.111/b28129/feature_extr.htm#i1005920
Attribute importance is a supervised function that ranks attributes
according to their significance in
