I have come across similar situation recently and decided to run Training workflow less frequently than scoring workflow.
In your use case I would imagine you will run IDF fit workflow once in say a week. It will produce a model object which will be saved. In scoring workflow, you will typically see new unseen dataset and the model generated in training flow will be used to score or label this new dataset. Note, train and test datasets are used during development phase when you are trying to find out which model to use and efficientcy/performance/accuracy etc. It will never be part of workflow. In a little elaborate setting you may want to automate model evaluations, but that's a different story. Not sure if I could explain properly, please feel free to comment. On 1 Nov 2016 22:54, "Nirav Patel" <npa...@xactlycorp.com> wrote: > Yes, I do apply NaiveBayes after IDF . > > " you can re-train (fit) on all your data before applying it to unseen > data." Did you mean I can reuse that model to Transform both training and > test data? > > Here's the process: > > Datasets: > > 1. Full sample data (labeled) > 2. Training (labeled) > 3. Test (labeled) > 4. Unseen (non-labeled) > > Here are two workflow options I see: > > Option - 1 (currently using) > > 1. Fit IDF model (idf-1) on full Sample data > 2. Apply(Transform) idf-1 on full sample data > 3. Split data set into Training and Test data > 4. Fit ML model on Training data > 5. Apply(Transform) model on Test data > 6. Apply(Transform) idf-1 on Unseen data > 7. Apply(Transform) model on Unseen data > > Option - 2 > > 1. Split sample data into Training and Test data > 2. Fit IDF model (idf-1) only on training data > 3. Apply(Transform) idf-1 on training data > 4. Apply(Transform) idf-1 on test data > 5. Fit ML model on Training data > 6. Apply(Transform) model on Test data > 7. Apply(Transform) idf-1 on Unseen data > 8. Apply(Transform) model on Unseen data > > So you are suggesting Option-2 in this particular case, right? > > On Tue, Nov 1, 2016 at 4:24 AM, Robin East <robin.e...@xense.co.uk> wrote: > >> Fit it on training data to evaluate the model. You can either use that >> model to apply to unseen data or you can re-train (fit) on all your data >> before applying it to unseen data. >> >> fit and transform are 2 different things: fit creates a model, transform >> applies a model to data to create transformed output. If you are using your >> training data in a subsequent step (e.g. running logistic regression or >> some other machine learning algorithm) then you need to transform your >> training data using the IDF model before passing it through the next step. >> >> ------------------------------------------------------------ >> ------------------- >> 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 11:18, Nirav Patel <npa...@xactlycorp.com> wrote: >> >> 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.e...@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 <npa...@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 <npa...@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 >>>> >>> >>> >>> >>> >>> [image: What's New with Xactly] <http://www.xactlycorp.com/email-click/> >>> >>> <https://www.nyse.com/quote/XNYS:XTLY> [image: LinkedIn] >>> <https://www.linkedin.com/company/xactly-corporation> [image: Twitter] >>> <https://twitter.com/Xactly> [image: Facebook] >>> <https://www.facebook.com/XactlyCorp> [image: YouTube] >>> <http://www.youtube.com/xactlycorporation> >>> >>> >>> >> >> >> >> [image: What's New with Xactly] <http://www.xactlycorp.com/email-click/> >> >> <https://www.nyse.com/quote/XNYS:XTLY> [image: LinkedIn] >> <https://www.linkedin.com/company/xactly-corporation> [image: Twitter] >> <https://twitter.com/Xactly> [image: Facebook] >> <https://www.facebook.com/XactlyCorp> [image: YouTube] >> <http://www.youtube.com/xactlycorporation> >> >> >> > > > > [image: What's New with Xactly] <http://www.xactlycorp.com/email-click/> > > <https://www.nyse.com/quote/XNYS:XTLY> [image: LinkedIn] > <https://www.linkedin.com/company/xactly-corporation> [image: Twitter] > <https://twitter.com/Xactly> [image: Facebook] > <https://www.facebook.com/XactlyCorp> [image: YouTube] > <http://www.youtube.com/xactlycorporation>
