😎 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

Efficient Permutation-based GWAS for Normal and Skewed Phenotypic Distributions

Official Implementation of ACMMM'21 paper "Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting"

pylambertw - sklearn interface to analyze and gaussianize heavy-tailed, skewed data

LambertW R package: Lambert W x F distributions and Gaussianization for skewed & heavy-tailed data

Quantum ML for extremely imbalanced data

data engineering challenges and fun

Fix data skew by packing into bins

Skin lesion image analysis that draws on meta-learning to improve performance in the low data and imbalanced data regimes.

Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

Space-Time Statistical Quality Control of Extreme Precipitation Observation

Machine Learning Nano-degree Project : To help a charity organization identify people most likely to donate to their cause

Build predictive models on highly skewed data by selecting an example of fraudulent transactions in the financial institutions🚀

Course Major Project of Pattern Recognition and Machine Learning( CSL2050 )

A base possui informações obtidas de análises químicas de vinhos da mesma região da Itália, porém são provenientes de 3 diferentes cultivadores. A análise mostra a quantidade de 13 componentes achados em cada um dos 3 tipos de vinhos.

Trying to recogize and predict fraud in financial transactions is a good example of binary classification analysis. A transaction either is fraudulent, or it is genuine. What makes fraud detection especially challenging is the is the highly imbalanced distribution between positive (genuine) and negative (fraud) classes.

Predicting Time of Arrival for Food Delivery Service