semvec/pypi-packages huggingface dataset:
PiML 0.6.0.post2
A low-code interpretable machine learning toolbox in Python.
License: Apache

<div align="center">
  
<img src="https://raw.githubusercontent.com/SelfExplainML/PiML-Toolbox/master/examples/results/LogoPiML.png" alt="drawing" width="314.15926"/>

**An integrated Python toolbox for interpretable machine learning** 

`pip install PiML`
  
<g-emoji class="g-emoji" alias="rocket" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f680.png">🚀</g-emoji> **October 31, 2022:**  V0.4.0 is released with enriched models and enhanced diagnostics.

<g-emoji class="g-emoji" alias="rocket" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f680.png">🚀</g-emoji> **July 26, 2022:**  V0.3.0 is released with classic statistical models.

<g-emoji class="g-emoji" alias="rocket" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f680.png">🚀</g-emoji> **June 26, 2022:** V0.2.0 is released with high-code APIs.

<g-emoji class="g-emoji" alias="loudspeaker" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4e2.png">📢</g-emoji> **May 4, 2022:**  V0.1.0 is launched with low-code UI/UX.
</div>

PiML (or π-ML, /ˈpaɪ·ˈem·ˈel/) is a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports a growing list of inherently interpretable ML models:

1. **GLM**: Linear/Logistic Regression with L1 ∨ L2 Regularization
1. **GAM**: Generalized Additive Models using B-splines
1. **Tree**: Decision Tree for Classification and Regression
2. **FIGS**: Fast Interpretable Greedy-Tree Sums (Tan, et al. 2022)
3. **XGB2**: Extreme Gradient Boosted Trees of Depth 2 (Chen and Guestrin, 2016; Lengerich, et al. 2020)
4. **EBM**: Explainable Boosting Machine (Nori, et al. 2019; Lou, et al. 2013)
5. **GAMI-Net**: Generalized Additive Model with Structured Interactions (Yang, Zhang and Sudjianto, 2021)
6. **ReLU-DNN**: Deep ReLU Networks using Aletheia Unwrapper and Sparsification (Sudjianto, et al. 2020)

PiML also works for arbitrary supervised ML models under regression and binary classification settings. It supports a whole spectrum of outcome testing, including but not limited to, the following:

1. **Accuracy**: popular metrics like MSE, MAE for regression tasks and ACC, AUC, Recall, Precision, F1-score for binary classification tasks. 
1. **Explainability**: post-hoc global explainers (PFI, PDP, ALE) and local explainers (LIME, SHAP).
1. **Fairness**: disparity test and segmented analysis by integrating the solas-ai package.
1. **WeakSpot**: identification of weak regions with high residuals by slicing techniques.
2. **Overfit**: identification of overfitting regions according ot train-test performance gap.
3. **Reliability**: assessment of prediction uncertainty by split conformal prediction techniques.
4. **Robustness**: evaluation of performance degradation under covariate noise perturbation.
5. **Resilience**: evaluation of performance degradation under different out-of-distribution scenarios.