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A small package for automatic model training

Project description

auto-modelling

This repo is a simple version of parameter tuning.

reference: https://github.com/EpistasisLab/tpot/blob

Quick set-up

  • Clone the repo

  • Create the virtual environment

mkvirtualenv auto-train
workon auto-train
pip install requirements.txt

install xgboost refrence: https://xgboost.readthedocs.io/en/latest/build.html# https://www.ibm.com/developerworks/community/blogs/jfp/entry/Installing_XGBoost_on_Mac_OSX?lang=en

Note

  • TO DO: Feature selection, pre-processing

Thoughts

  • Ideally, any dataframe being throw into this repo, it should be
  1. pre-processing

    • drop column that have too many null
    • fill na for both numeric and non-numeric values
    • encoded for non-numeric values
    • scale values if needed
    • balance the dataset if needed
  2. model-training

    • mode = classification, regression, auto
    • split data-set
    • tuning parameters and model selection
    • feature selection
    • return a model with parameters, columns and a script to process x_test

Project details


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