Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.

This project classifies diseases in grape plant using various Machine Learning classification algorithms.

Face Morphing Attack Detection Benchmark (IJCB 2022: Robust Ensemble Morph Detection with Domain Generalization)

Real-time fraud detection system using ensemble ML models, featuring streaming data processing, explainable AI with SHAP, and production-ready deployment with FastAPI and Docker.

Flexible and transparent Python Boruta implementation

[In Progress] Code for the paper-- MTDeep: boosting the security of deep neural nets against adversarial attacks with moving target defense

An ensemble of BERTs for classifying injury narratives

Experiment on dynamic selection of classifiers in multiple stages

Experiment on the pruning of pool of classifiers

A research-based voice analysis platform for mental health screening using machine learning

UBayFS in Python

Experiment on the generation of pools of classifiers

End-to-end MLOps pipeline implementing grid search cross-validation with multi-algorithm ensemble selection for wine quality regression.

Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance

A Machine Learning project to predict the success or failure of startups based on data by using ensemble modeling techniques, MLflow for tracking experiments, Docker for containerization.

A deployed machine learning model that has the capability to automatically classify the incoming disaster messages into related 36 categories. Project developed as a part of Udacity's Data Science Nanodegree program.

Personal implementation of the paper "A two-stage ensemble method for the detection of class-label noise"

A comprehensive set of programs demonstrating machine learning techniques have been made.

This repository contains the implementation of a Mixed Capacity Ensemble (MCE) framework for mitigating dataset artifacts in natural language inference (NLI) tasks. The project focuses on identifying dataset artifacts, training robust models, and evaluating their performance using datasets like SNLI.