Combining tree-boosting with Gaussian process and mixed effects models

The Steel Plates Faults dataset project utilizes machine learning to enhance quality control in steel manufacturing, aiming to develop models for efficient fault detection and classification. This initiative promises to improve productivity and reduce costs, ensuring the delivery of high-quality steel products to meet industry demands.

Project building ML & DL models to detect spam messages.

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

This project leverages data from 383 thyroid cancer patients over 15 years to develop a model to predict propensity for reoccurrence based on certain features. This work extends and further explores different model types to emerge with the best predictor model - and save more lives

A fraud detection (exploratory) project using machine learning algorithms over 11 million real transactions

"This repository contains implementations of Boosting method, popular techniques in Model Ensembles, aimed at improving predictive performance by combining multiple models. by using titanic database."

A collection of boosting algorithms written in Rust 🦀

This repository contains a comprehensive guide and implementation of ensemble modeling techniques, specifically focusing on Boosting, Bagging, and Voting. Ensemble methods are powerful techniques in machine learning that combine the predictions from multiple models to improve overall performance and robustness.

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

👩‍💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.

Our goal in this project was to develop statistical and machine learning models to replicate the functionality of the traditional Black-Scholes option pricing formula, specifically for valuing European call options.

Python files employed in my research

R markdown files employed in my research

This notebook explores fraud detection using various machine learning techniques.

Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it

Профильное Задание VK

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection