You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/09/24 02:09:04 UTC
[jira] [Updated] (SPARK-5890) Add QuantileDiscretizer
[ https://issues.apache.org/jira/browse/SPARK-5890?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley updated SPARK-5890:
-------------------------------------
Description:
A `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned categorical features.
{code}
val fd = new QuantileDiscretizer()
.setInputCol("age")
.setNumBins(32)
.setOutputCol("ageBins")
{code}
This should an automatic feature discretizer, which uses a simple algorithm like approximate quantiles to discretize features. It should set the ML attribute correctly in the output column.
was:
A `FeatureDiscretizer` takes a column with continuous features and outputs a column with binned categorical features.
{code}
val fd = new FeatureDiscretizer()
.setInputCol("age")
.setNumBins(32)
.setOutputCol("ageBins")
{code}
This should an automatic feature discretizer, which uses a simple algorithm like approximate quantiles to discretize features. It should set the ML attribute correctly in the output column.
> Add QuantileDiscretizer
> -----------------------
>
> Key: SPARK-5890
> URL: https://issues.apache.org/jira/browse/SPARK-5890
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Xusen Yin
>
> A `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned categorical features.
> {code}
> val fd = new QuantileDiscretizer()
> .setInputCol("age")
> .setNumBins(32)
> .setOutputCol("ageBins")
> {code}
> This should an automatic feature discretizer, which uses a simple algorithm like approximate quantiles to discretize features. It should set the ML attribute correctly in the output column.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org