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From Nirav Patel <npa...@xactlycorp.com>
Subject Re: Spark ML - Is IDF model reusable
Date Tue, 01 Nov 2016 11:18:00 GMT
Just to re-iterate what you said, I should fit IDF model only on training
data and then re-use it for both test data and then later on unseen data to
make predictions.

On Tue, Nov 1, 2016 at 3:49 AM, Robin East <robin.east@xense.co.uk> wrote:

> The point of setting aside a portion of your data as a test set is to try
> and mimic applying your model to unseen data. If you fit your IDF model to
> all your data, any evaluation you perform on your test set is likely to
> over perform compared to ‘real’ unseen data. Effectively you would have
> overfit your model.
> ------------------------------------------------------------
> -------------------
> Robin East
> *Spark GraphX in Action* Michael Malak and Robin East
> Manning Publications Co.
> http://www.manning.com/books/spark-graphx-in-action
>
>
>
>
>
> On 1 Nov 2016, at 10:15, Nirav Patel <npatel@xactlycorp.com> wrote:
>
> FYI, I do reuse IDF model while making prediction against new unlabeled
> data but not between training and test data while training a model.
>
> On Tue, Nov 1, 2016 at 3:10 AM, Nirav Patel <npatel@xactlycorp.com> wrote:
>
>> I am using IDF estimator/model (TF-IDF) to convert text features into
>> vectors. Currently, I fit IDF model on all sample data and then transform
>> them. I read somewhere that I should split my data into training and test
>> before fitting IDF model; Fit IDF only on training data and then use same
>> transformer to transform training and test data.
>> This raise more questions:
>> 1) Why would you do that? What exactly do IDF learn during fitting
>> process that it can reuse to transform any new dataset. Perhaps idea is to
>> keep same value for |D| and DF|t, D| while use new TF|t, D| ?
>> 2) If not then fitting and transforming seems redundant for IDF model
>>
>
>
>
>
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