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explainable-ml

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George3d6
George3d6 commented Mar 9, 2020

Currently as per #403 , lightwood (and by extension mindsdb) fails to install on 32bit python environments.

We should see if there's an easy way to make it work, since 32 bits might still be used for a long time on various embedded device.

If there isn't (or if there is, but it takes too long to implement support) we should add a notification to the docs that you need 64 bit python (where we

DALEX
pbiecek
pbiecek commented Mar 14, 2020

General:

  • remove outdated examples from DALEX_docs
  • prepare skeleton for R/Python docs

R specific:

  • prepare Introductory materials to predictive models for titanic and apartments
  • prepare Introductory materials to explain()
  • prepare Introductory materials to predict_parts()
  • prepare Introductory materials to predict_profile()
  • prepar
dvorka
dvorka commented Sep 21, 2018

In order to successfully install examples using Docker I did the following changes:

  • There seems to be missing step which clones mli-resources GitHub repository. Perhaps RUN git clone https://github.com/h2oai/mli-resources.git should be added to Dockerfile (I cloned repo manually).
  • Jupyter refuses to start under root - consider adding --allow-root parameter: `docker run -i -t -p 888

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

  • Updated Nov 25, 2019
  • Python

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