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What does the GitHub action do? 📢

It automates your Machine Learning model training, with the help of autofeat python package. This GitHub action makes use of AutoFeatClassifier() to build to the classification model.

Disclaimer: For models with very low samples, they might overfit on noise in the dataset, i.e., find some new features that lead to a good prediction on the training set but result in poor performance on new test samples.

How to use? 💁

You will find my "Automated ML Models using Autofeat package" action in the marketplace. Then follow these simple steps:

• Click on Use latest version, a prompt will be displayed. We need to copy the code and use it in our workflow as shown below.
• Make your workflow as shown below. (I have used the Iris dataset as an example)

name: Iris Dataset Classifier
on: [push]
jobs:
  build_model:
    runs-on: ubuntu-latest
    steps:
    - name: Train the model
      id: model
      uses: Haimantika/random@master
      with:
        myInput: "[5.1,3.5,1.4,2.1,1.8,0.2]"
    - name: Upload artifact
      uses: actions/upload-artifact@v2
      with:
        name: my-artifact
        path: model.pkl

• Create a repository and upload the dataset to train the model. The name of the dataset file must be dataset.csv
• Go to Actions on GitHub Console and click on Set up a workflow yourself.
• Click on Start commit, give a commit message (optional) and click on Commit new file. The workflow begins soon after this.
• Click on the workflow and you’ll get the console output. You can click on Artifacts and download the model file keep it for later use.