forestry-labs/distillML
An R package providing functions for interpreting and distilling machine learning models
This project helps data scientists and analysts understand how their complex machine learning models make predictions. It takes an existing "black box" model and training data as input, and outputs visual explanations like Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) curves, and Accumulated Local Effects (ALE) plots. You would use this to explain model behavior and build trust in your predictions.
No commits in the last 6 months.
Use this if you need to explain the reasoning behind predictions from any supervised machine learning model to stakeholders or for regulatory compliance.
Not ideal if you are looking for methods to improve model performance or to select features for building a new model.
Stars
9
Forks
2
Language
R
License
—
Category
Last pushed
Apr 19, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/forestry-labs/distillML"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of...
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research...
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
cdt15/lingam
Python package for causal discovery based on LiNGAM.
andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python