Yes, the user is responsible for using the correct model for a given piece
of testing (or unlabeled) data.
2013/12/2 unmesha sreeveni unmeshab...@gmail.com
To make it more general, it's better to separate them. Since there might
be multiple batches of training (or to-be-label), and you only
Thank you Yexi...Thanks for spending your valuable time.
On Mon, Dec 2, 2013 at 8:22 PM, Yexi Jiang yexiji...@gmail.com wrote:
Yes, the user is responsible for using the correct model for a given piece
of testing (or unlabeled) data.
2013/12/2 unmesha sreeveni unmeshab...@gmail.com
To
Thanks Yexi ,
But how it can be accomplished.
The input to Desicion Tree MR will be a set of data. But while
predicting a data it will be a one line data without classlabel right?
So what changes will be there in mrjob.Should we design like this.
1. When a set of data is coming draw Desicion
In my opinion.
1. Build the decision tree model with the training data.
2. Store it somewhere.
3. When the unlabeled data is available:
3.1 if the unlabeled data is huge, write another mrjob to process them,
load the model at the setup stage, use the model to label the data one by
one in map
Thanks Yexi...A very nice explanation...Thanks a lot..
Explained in a very simple way which is really understandable for
beginners..Thanks a lot.
I can go for chaining jobs right?
On Sun, Dec 1, 2013 at 8:55 PM, Yexi Jiang yexiji...@gmail.com wrote:
In my opinion.
1. Build the decision
What is your motivation of using chaining jobs?
2013/12/1 unmesha sreeveni unmeshab...@gmail.com
Thanks Yexi...A very nice explanation...Thanks a lot..
Explained in a very simple way which is really understandable for
beginners..Thanks a lot.
I can go for chaining jobs right?
On Sun,
1. I jst thought of building a model using a project named say DT and wen a
huge input comes do another mr job test.java with in DT.
If not chaining jobs we need to create seperate project right DT_build and
DT_test projects
NO need for seperate project file?
2. M1_train - dataset for training.
Actually the training and testing (or prediction) are not necessary to be
done in one shot. If you need to do them consecutively in your particular
scenario, you can do it as what you said.
To make it more general, it's better to separate them. Since there might be
multiple batches of training
To make it more general, it's better to separate them. Since there might be
multiple batches of training (or to-be-label), and you only need to train
the model once (if your data is stable).
Ok , I will go for the second one.
So if we are going for separate.They will not have any connection with
I have gone through a Map Reduce implementation of c4.5 in
http://btechfreakz.blogspot.in/2013/04/implementation-of-c45-algorithm-using.html
Here a decision tree is build. So my doubt is
Can we also include the prediction along with that?
On Tue, Nov 26, 2013 at 8:52 AM, Yexi Jiang
As far as I know, there is no ID3 implementation in mahout currently, but
you can use the decision forest instead.
https://cwiki.apache.org/confluence/display/MAHOUT/Breiman+Example.
2013/11/25 unmesha sreeveni unmeshab...@gmail.com
Is that ID3 classification?
It includes prediction also?
ok . Thx Yexi
On Tue, Nov 26, 2013 at 1:41 AM, Yexi Jiang yexiji...@gmail.com wrote:
As far as I know, there is no ID3 implementation in mahout currently, but
you can use the decision forest instead.
https://cwiki.apache.org/confluence/display/MAHOUT/Breiman+Example.
2013/11/25 unmesha
You are welcome :)
2013/11/25 unmesha sreeveni unmeshab...@gmail.com
ok . Thx Yexi
On Tue, Nov 26, 2013 at 1:41 AM, Yexi Jiang yexiji...@gmail.com wrote:
As far as I know, there is no ID3 implementation in mahout currently, but
you can use the decision forest instead.
Is that ID3 classification?
It includes prediction also?
On Sat, Nov 23, 2013 at 9:01 PM, Yexi Jiang yexiji...@gmail.com wrote:
You can directly find it at https://github.com/apache/mahout, or you can
check out from svn by following
I want to go through Decision tree implementation in mahout. Refereed Apache
Mahout http://mahout.apache.org/
6 Feb 2012 - Apache Mahout 0.6 released
Apache Mahout has reached version 0.6. All developers are encouraged
to begin using version 0.6. Highlights include:
Improved Decision Tree
You can directly find it at https://github.com/apache/mahout, or you can
check out from svn by following
https://cwiki.apache.org/confluence/display/MAHOUT/Version+Control.
2013/11/23 unmesha sreeveni unmeshab...@gmail.com
I want to go through Decision tree implementation in mahout. Refereed
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