InData And BeyondbyDima Iakubovskyi·Oct 15Class Weights 101: The One-Line Fix for Imbalanced Data That Beats SMOTEOne keyword argument that also rewrites your predicted probabilities
Rohan Jain·Oct 12Predicting Trade Settlement Fails: What I’d Do Differently, From SVM + SMOTE to XGBoost + SHAPI rebuilt a classic ML model, this time on 3.1 million synthetic trades where I knew the right answers.
Maria Victor·Oct 7Aprendizado de Máquina, Versão Feira Livre — Parte 3Da banca de laranjas ao modelo: como o computador aprende a separar a boa da podre
Nour El-Rouby·Aug 18Why 99.8% Accuracy Can Still Be a Terrible Fraud Detection ModelA credit card fraud project showed me why accuracy can be dangerously misleading and which metrics actually matter.
Mr. Vivek Kumar·Aug 13Handling Imbalanced Data in MLSMOTE, ADASYN, Threshold Tuning, Class Weights, NCL, ENN, Tomek Links, etc.
VectorWorks Academy·Jul 26Focal Loss Explained: When It Helps, When It Hurts, and How to Think About It in Production MLOne of the first challenges engineers encounter when training large-scale machine learning models is class imbalance. Suppose you’re…A response icon1A response icon1
InLevel Up CodingbyOğuz Kaan Mavice·Jul 10A Data Platform Engineer’s AI RebuildChapter 1 : What a 73%-Accurate ‘Do-Nothing’ Model Taught Me Before the Real Work StartedA response icon1A response icon1
InData And BeyondbyChao De-Yu·Jun 7Why 99% Accuracy Can Be Misleading: The Accuracy Paradox in Machine LearningHow imbalanced data creates the illusion of a perfect modelA response icon1A response icon1
Emre Koçyiğit·Jun 1Handling Imbalanced Data: Mindset Before TechniquesSome topics in ML feel familiar until someone asks you to explain them clearly. Imbalanced datasets is one of them. While my recent writing…
For Data Science·May 22Dealing with Imbalanced Datasets: Every Technique You Need to KnowYour model says 98% accuracy. Your fraud detection catches nothing.
