simsal0r/mixture-of-decision-trees
Mixture of Decision Trees for Interpretable Machine Learning
This project helps data scientists and machine learning engineers build more understandable predictive models. It takes your dataset as input and generates a collection of specialized decision trees, along with a 'gating' function that explains which tree is relevant for different types of data. This allows for clear explanations of how a prediction is made, making complex models transparent.
No commits in the last 6 months.
Use this if you need to build predictive models where understanding the 'why' behind each prediction is as important as the prediction itself, especially for tasks requiring regulatory compliance or human trust.
Not ideal if your primary goal is maximizing predictive accuracy at all costs, and model interpretability is not a key requirement.
Stars
11
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Sep 02, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/simsal0r/mixture-of-decision-trees"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
google/yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random...
parrt/dtreeviz
A python library for decision tree visualization and model interpretation.
tensorflow/decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of...
neurodata/treeple
Scikit-learn compatible decision trees beyond those offered in scikit-learn
winkjs/wink-regression-tree
Decision Tree to predict the value of a continuous target variable