Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark

Fast inference of Boosted Decision Trees in FPGAs

These are my notes for the interview prep workshop I led on Random Forests

This is a Credit Analysis project developed by Felipe Solares da Silva and is part of his professional portfolio.

Following Project is for predicting the list of creditworthy customers for a bank.

S-BDT: Distributed Differentially Private Boosted Decision Trees

All the code used for my MSc Thesis: Search for Dark Matter using Machine Learning in dilepton and missing energy events with the ATLAS detector at the LHC, A tentative model independent approach

Making Prediction from fivethirtyeight 2016 Election Poll Data

Research based testing of Boosted Tree Classifier for Predicting Disease from Symptoms

This is the repository for Jay Shreedhar, Varun Thakur, Tara Gopinath, and Yueyang Pan's final project on diabetes risk classification for DSCI 550 - Data Science At Scale at USC. Check the individual contributor branches for reports on each classification model.

Given financial information of a person, this determines, based on past data (through boosted decision trees), whether or not to approve their loan request.

Classification Trees, Random Forest, Boosting | Columbia Business School

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

Prediction of Breast Cancer using Logistic Regression/Decision Trees/Boosted Decision Trees

Future Ready Talent Project Submission.Using Azure ML Studio to predict the income of individuals, based on their age, race, education, residence city, etc. Used the adult census dataset

Codes for reproducing the results of arXiv:2207.04157

A sigmoid SVM classifier for predicting whether an online post is conspiracy or philosophical theory

Collection of notebooks accompanying a research paper on evaluating GHG emissions from hydroelectric, multipurpose and irrigation reservoirs in Myanmar

A few classifiers - ML (level basic); scikit-learn

📊 Simple Supervised & Unsupervised Machine Learning Models to be applied to Static and Time Series datasets.