Bhatwar195/Ensemble-Learning-Techniques-ML
Implementation and explanation of Ensemble Learning algorithms including Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and Stacking with Python and practical examples.
Stars
1
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 18, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Bhatwar195/Ensemble-Learning-Techniques-ML"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
iamDecode/sklearn-pmml-model
A library to parse and convert PMML models into Scikit-learn estimators.
yzhao062/combo
(AAAI' 20) A Python Toolbox for Machine Learning Model Combination
flennerhag/mlens
ML-Ensemble – high performance ensemble learning
vecxoz/vecstack
Python package for stacking (machine learning technique)
enriquegit/multiviewstacking
A python implementation of the Multi-View Stacking algorithm