It looks like all of that is building up to spark 2.0 (for random forests /
gbts / etc.). Ah well...thanks for your help. Was interesting digging into the
depths.
Date: Wed, 13 Apr 2016 09:48:32 +0100
Subject: Re: ML Random Forest Classifier
From: ja...@gluru.co
To: as...@live.com
CC: user
n workarounds? It's looking like I'll have to revert to
> using mllib instead :(
>
> -Ashic.
>
> --
> From: as...@live.com
> To: ja...@gluru.co
> CC: user@spark.apache.org
> Subject: RE: ML Random Forest Classifier
> Date: Wed, 13 Apr 2016 02
instead :(
-Ashic.
From: as...@live.com
To: ja...@gluru.co
CC: user@spark.apache.org
Subject: RE: ML Random Forest Classifier
Date: Wed, 13 Apr 2016 02:20:53 +0100
I managed to get to the map using MetadataUtils (it's a private ml package).
There are still some issues, around feature names
I managed to get to the map using MetadataUtils (it's a private ml package).
There are still some issues, around feature names, etc. Trying to pin them down.
From: as...@live.com
To: ja...@gluru.co
CC: user@spark.apache.org
Subject: RE: ML Random Forest Classifier
Date: Wed, 13 Apr 2016 00:50:31
, but I was hoping for a more automated way of
getting the map. Since the trained model already knows about the value, perhaps
it's possible to grab it for storage?
Thanks,Ashic.
From: as...@live.com
To: ja...@gluru.co
CC: user@spark.apache.org
Subject: RE: ML Random Forest Classifier
Date: Mon, 11
Thanks, James. That looks promising.
Date: Mon, 11 Apr 2016 10:41:07 +0100
Subject: Re: ML Random Forest Classifier
From: ja...@gluru.co
To: as...@live.com
CC: user@spark.apache.org
To add a bit more detail perhaps something like this might work:
package org.apache.spark.ml
import
To add a bit more detail perhaps something like this might work:
package org.apache.spark.ml
>
>
> import org.apache.spark.ml.classification.RandomForestClassificationModel
>
> import org.apache.spark.ml.classification.DecisionTreeClassificationModel
>
> import
There are methods for converting the dataframe based random forest models
to the old RDD based models and vice versa. Perhaps using these will help
given that the old models can be saved and loaded?
In order to use them however you will need to write code in the
org.apache.spark.ml package.
I've
Hello,I'm trying to save a pipeline with a random forest classifier. If I try
to save the pipeline, it complains that the classifier is not Writable, and
indeed the classifier itself doesn't have a write function. There's a pull
request that's been merged that enables this for Spark 2.0 (any
