Skip to main content

A small package for automatic model training

Project description


This repo is a simple version of parameter tuning.


Quick set-up

  • Clone the repo

  • Create the virtual environment

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

install xgboost refrence:


  • TO DO: Feature selection, pre-processing


  • 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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
auto_modelling-1.0.0-py3-none-any.whl (7.1 kB) Copy SHA256 hash SHA256 Wheel py3
auto_modelling-1.0.0.tar.gz (2.8 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page