https://github.com/sayakpaul/benchmarking-and-mli-experiments-on-the-adult-dataset

Contains benchmarking and interpretability experiments on the Adult dataset using several libraries
https://github.com/sayakpaul/benchmarking-and-mli-experiments-on-the-adult-dataset

data-science fastai h2oai interpretable-machine-learning machine-learning microsoft-interpret tensorflow

Last synced: 23 days ago
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Contains benchmarking and interpretability experiments on the Adult dataset using several libraries

• Host: GitHub
• URL: https://github.com/sayakpaul/benchmarking-and-mli-experiments-on-the-adult-dataset
• Owner: sayakpaul
• Created: 2017-09-17T07:25:44.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-05-19T01:03:19.000Z (over 5 years ago)
• Last Synced: 2024-10-03T12:38:20.665Z (about 1 month ago)
• Topics: data-science, fastai, h2oai, interpretable-machine-learning, machine-learning, microsoft-interpret, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 869 KB
• Stars: 34
• Watchers: 3
• Forks: 12
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
The initial experiments were a part of an assignment given from TCS ILP Innovations' Lab. Later as my appetite for the wonderful field of machine learning increased, I decided to give it another try and try out the new libraries. 

It includes benchmarking and interpretability experiments on the [Adult Data set](https://archive.ics.uci.edu/ml/datasets/adult) using libraries like [`fastai`](docs.fast.ai), [`h2o`](http://docs.h2o.ai) and [`interpret`](https://github.com/Microsoft/interpret). Along with these, I have shown how one can use the `interpret` library to construct explanations for `sklearn` models. **Note** that `keras` models can be converted to `sklearn` variants and this enables `interpret` to work equally on these models as well. 

I show you how easy it is to interpret a blackbox machine learning model with `interpret`. I think the library really stands its name. Along with this, I also show how to use Decision Tree Surrogate to explain models in `h2o`.

To do:

**Annotate the notebooks in plain English and include short explanations to the various interpretability methods used.**