This repository contains the code, documentation, and datasets for a comprehensive exploration of machine learning techniques to address class imbalance. The project investigates the impact of various methods, like ADASYN, KMeansSMOTE, and Deep Learning Generator, on classification performance while effectively demonstrating benefits of pipelining.

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

Toxicity detection on imbalanced social media data. Focused on 2 main topics of toxicity detection: Class imbalance problem and detecting toxic comments.

Parametric Contrastive Learning (ICCV2021) & GPaCo (TPAMI 2023)

A CNN model to identify images of plant seedlings.

The Learning vector quantization (LVQ) is a prototype-based supervised classification algorithm proposed by T. Kohonen.

🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库

Python package for tackling multi-class imbalance problems. http:https://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

MICCAI 2023: DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.

[NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库

Evaluating ensemble performance in long-tailed datasets (Neurips 2023 Heavy Tails Workshop)

Build a classification model for reducing the churn rate for a telecom company

A predictive model to anticipate customer churn in telecom. Using supervised ML techniques, it identifies at-risk customers based on usage patterns and service plans. Proactively retaining customers, reducing attrition costs.

A Julia toolbox with resampling methods to correct for class imbalance.

R and Data Files from my YouTube Channel

Repo for 2024 peak cherry blossom prediction competition

This homework leverages SMOTE for addressing class imbalance in a high-dimensional dataset, employing tree-based methods like random forest and XGBoost with model trees to enhance classification performance on the APS Failure at Scania Trucks dataset.

Predict whether customer purchase a product or not in a session