A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

This Repository consists of programs related to AI-ML-DL-NLP-CV. Few examples include - KNN, Naive Bayes, Decision Trees, EDA etc.

Stakeholder-Specific Vulnerability Categorization

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

The programs of certificate course on "Artificial Intelligence and Machine Learning" conducted at CDAC for DRDO by Tushar B. Kute.

Beta Machine Learning Toolkit

Implementation of Dynamic Programming Decision Tree algorithm (Kohler et. al. 2024).

Scikit-learn compatible decision trees beyond those offered in scikit-learn

This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.

This project focuses on developing machine learning solutions for various use cases within 5G New Radio (NR) networks, specifically under the Open Radio Access Network (O-RAN) framework.

A recommender system project for restaurants utilizing a machine learning chatbot.

This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.

An R package to create and visualise fast-and-frugal decision trees (FFTs)

BRAND NEW: SAT Optimization Decision Tree Library for Interpretable Clustering and Classification

Tools created for machine learning classification model evaluation

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

A project aimed at predicting employee promotions using Automated ML techniques with the EvalML library. The workflow includes data analysis, feature engineering, model building with Random Forest, Logistic Regression, Decision Tree, and AutoML evaluation to determine the best-performing pipeline.

SnapML library's Decision Tree classifier and SVM was used to train a model on a real dataset to identify fraudulent credit card transactions. The Decision Tree model resulted in ROC-AUC score = 0.92 and the SVM yielded ROC-AUC score = 0.93 and hinge loss = 0.15. Multi-threaded CPU was implemented to reduce model training time.

🍊 📊 💡 Orange: Interactive data analysis