cerlymarco/linear-tree
A python library to build Model Trees with Linear Models at the leaves.
This tool helps data scientists and machine learning engineers build more accurate and interpretable predictive models. It takes your existing tabular dataset, processes it using advanced tree-based algorithms with linear models, and outputs improved predictions or classifications. It's designed for practitioners who already use Python and scikit-learn for their machine learning tasks.
389 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to improve the predictive power and interpretability of your models, especially when traditional linear models are too simplistic and standard decision trees lack the desired precision.
Not ideal if you are looking for a no-code solution or are new to machine learning and Python programming.
Stars
389
Forks
60
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 19, 2024
Commits (30d)
0
Dependencies
3
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/cerlymarco/linear-tree"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
grf-labs/grf
Generalized Random Forests
LAMDA-NJU/Deep-Forest
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
benedekrozemberczki/awesome-decision-tree-papers
A collection of research papers on decision, classification and regression trees with implementations.
ysraell/random-forest-mc
Random Forest with Dynamic Tree Selection Monte Carlo Based (RF-TSMC).
zhaoxingfeng/RandomForest
随机森林，Random Forest（RF）