Recursive partitioning (tree models) of psychometric networks

Introduction to tree models with Python

Comparing sampling techniques and classification algorithms to predict credit risk

Borsa istanbul şirketleri hakkında trend ve fiyat tahmin çalışması

Package confeito provides fast ensemble tree inference

Assignments from NIS-08-04 :: Machine Learning University Course, covering Linear Models, Tree Models, and Ensemble Methods.

A case study to identify the major factors which indicate that a customer is about to leave a mobile network. Subsequently, a model is built to predict churn customers in the future.

Machine Learning Class at Harding Univeristy

Overview of different machine learning algorithms, both supervised and unsupervised. Includes exploratory data analysis and performance evaluation of the models.

Feature attribution for realized model fit (rather than predictions). EDEF provides exact additive decomposition of predictive performance for linear, PyTorch, and tree-based models, including standard errors, grouped inference, and exact TreeIG decompositions for tree-based models.

This people analytics project analyzes factors influencing employee turnover and predicts whether an employee is likely to leave. It aims to uncover patterns behind departures, helping Salifort improve retention, workplace culture, and professional growth strategies.

Monthly flight delay forecasting using EDA, statistical analysis, feature engineering and regression modelling.

Algorithm wise projects

Machine learning models including decision trees, random forests, adaboost, gradient boosting, and neural networks applied to structured data for classification tasks.

Attempt at Training Tree models using Genetic Algorithms to generate AutoTrading Rules

RR-TreeLM v4 experiments for PPM-first tree language modeling

Exact Integrated Gradients for tree ensembles, including XGBoost, LightGBM, Random Forests, and Gradient Boosting. Computes fast, exact feature attributions for tree models without Monte Carlo sampling or path approximations.