PiML 0.2.2

A low-code interpretable machine learning toolbox in Python.

PiML: a low-code interpretable machine learning toolbox in Python

PiML (or π·ML, /ˈpaɪ·ˈem·ˈel/) is a new Python toolbox for Interpretable Machine Learning model development and validation. Through low-code automation and high-code programming, PiML supports various machine learning models in the following two categories:

• Inherently interpretable models:

1. EBM: Explainable Boosting Machine (Nori, et al. 2019; Lou, et al. 2013)
2. GAMI-Net: Generalized Additive Model with Structured Interactions (Yang, Zhang and Sudjianto, 2021)
3. ReLU-DNN: Deep ReLU Networks using Aletheia Unwrapper (Sudjianto, et al. 2020)

• Arbitrary black-box models，e.g.

1. LightGBM or XGBoost of varying depth
2. RandomForest of varying depth
3. Residual Deep Neural Networks

Low-code Examples

Click the ipynb links to run examples in Google Colab:

1. BikeSharing data: ipynb
2. CaliforniaHousing data: ipynb
3. TaiwanCredit data: ipynb

Begin your own PiML journey with this demo notebook.