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

Fast inference of Boosted Decision Trees in FPGAs

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

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

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

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

Classification Trees, Random Forest, Boosting | Columbia Business School

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

A real-time music genre classification system that combines classical machine learning techniques with a CNN trained on mel-spectrograms. The hybrid model utilizes DSP-based feature extraction and ensemble learning to achieve high accuracy and low latency for practical audio applications.

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

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

Implementation of decision trees for binary categorical data using numpy. Includes regular decision trees, random forest, and boosted trees.

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.

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

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

Making Prediction from fivethirtyeight 2016 Election Poll Data

Codes for reproducing the results of arXiv:2207.04157

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

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