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.

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

Fit interpretable models. Explain blackbox machine learning.

A game theoretic approach to explain the output of any machine learning model.

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.

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.

Tools created for machine learning classification model evaluation

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

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules

The objective of this DLM (Deep Learning Model) is to recognize the emotions from speech.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

In my code portfolio, I generally try new techniques and methods in machine learning. I don't like only copying and pasting.

Detect Credit Card Fraud with Machine Learning in R

Competing Risks and Survival Analysis

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

A website that chooses the best segment to advertise on

In the project we built the following algorithms : Decision Tree Classifiier and Regressor, AdaBoost Classifiier , Gradient Boost Regressor

In this project, a series of models were trained using six different classifiers from machine learning methods as well as artificial neural network (ANN) and convolutional neural network (CNN) for the detection and classification of brain tumours

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

mlim: single and multiple imputation with automated machine learning