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  1. Mahout
  2. MAHOUT-1941

Use the existing regression infrastructure to implement Logistic Regression using Samsara

    Details

    • Type: New Feature
    • Status: Open
    • Priority: Major
    • Resolution: Unresolved
    • Affects Version/s: 1.0.0
    • Fix Version/s: 1.0.0
    • Component/s: Classification
    • Labels:

      Description

      The goal is to reuse this chunk of mahout infrastructure to implement logistic regression: https://github.com/apache/mahout/tree/master/math-scala/src/main/scala/org/apache/mahout/math/algorithms/regression

      I need this for a work related POC and will get started by extending the RegressorModel.scala, will also put forward a design proposal on the dev mailing list.

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          Activity

          Hide
          kanjilal Saikat Kanjilal added a comment -

          Trevor Grant Since I need this for a work related POC I went ahead and created this JIRA to implement logistic regression based on the new algorithms object model that you created, I will send out a design on this JIRA as well as dev list first to review before beginning implementation, the goal is to implement logistic regression using multinomial/binary logistic regression

          Show
          kanjilal Saikat Kanjilal added a comment - Trevor Grant Since I need this for a work related POC I went ahead and created this JIRA to implement logistic regression based on the new algorithms object model that you created, I will send out a design on this JIRA as well as dev list first to review before beginning implementation, the goal is to implement logistic regression using multinomial/binary logistic regression
          Hide
          kanjilal Saikat Kanjilal added a comment -

          This issue will be solved when GLM is fully implemented at scale as that will include logistic regression

          Show
          kanjilal Saikat Kanjilal added a comment - This issue will be solved when GLM is fully implemented at scale as that will include logistic regression
          Hide
          rawkintrevo Trevor Grant added a comment -

          Should have replied here, not dev. Nice work- but a tip is to extend LinearRegressorFitter / Model.

          Even if you need to override for calculating standard error (since the Betas may not be normal) you'll avoid a lot of boilerplate code on the fitter tests.. You can just override as needed (e.g. you can override calculate stand error).

          It's also possible there are things in LinearRegressorModel (and Fitter) that really belong in OLS, and don't generalize to all linear models. I don't claim to be infallible

          Show
          rawkintrevo Trevor Grant added a comment - Should have replied here, not dev. Nice work- but a tip is to extend LinearRegressorFitter / Model. Even if you need to override for calculating standard error (since the Betas may not be normal) you'll avoid a lot of boilerplate code on the fitter tests.. You can just override as needed (e.g. you can override calculate stand error). It's also possible there are things in LinearRegressorModel (and Fitter) that really belong in OLS, and don't generalize to all linear models. I don't claim to be infallible

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            • Assignee:
              Unassigned
              Reporter:
              kanjilal Saikat Kanjilal
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              Dates

              • Created:
                Updated:

                Development