Boosted trees in Julia

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

Determining financial factors affecting the health of an individual

Custom built Decision Tree + Boosted Trees + KernelPLS in python

Regression model for predicting house prices of residential homes in Ames, Iowa. Dataset contains 79 explanatory variables. Project includes key topics such as dataset cleaning, feature selection/engineering, EDA and applying grid search to find the best model.

Using radial velocity data to identify exoplanet companions

Faster, better, smarter ecological niche modeling and species distribution modeling

GBDT (Gradient Boosted Decision Tree: 勾配ブースティング) のpythonによる実装

This is the repository for my R project on modeling historical weather data in Santa Barbara.

Classifying the survival of passengers aboard the Titanic via the use of various Machine Learning algorithms.

This is a project that was completed while taking the Udemy course - Python for Machine Learning & Data Science Masterclass.

Classification prediction model

Protein classification with deep learning and boosted trees using topological features

R based data analytics on German Cars Market

This project aims at developing, validating, and testing several classification statistical models that could predict whether or not an office room is occupied using several data features, namely temperature (◦C), light (lx), humidity (%), CO2 (ppm), and a humidity ratio. The data is modeled using classification techniques i.e. Logistic regressi…

Compare different classification models to predict the accuracy of identifying credit card transactions as normal or fraud

R code to reproduce analyses in "Rapid winter warming could disrupt coastal marine fish community structure" (Clark et al, Nature Climate Change, 2020)

implement machine learning algorithm from scratch

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]

Projects using tree methods (CART, Random Forests, Boosted Trees)