BBahati Brenda Kizito·Aug 1599% Accuracy Is Not Always Good: What Fraud Detection Taught Me About MLMy fraud detection model hit 99% accuracy and still failed. Here’s what imbalanced data, precision, recall, F1-score, confusion matrices, and SMOTE taught me the hard way.8 min read·godfident-data.hashnode.dev
JJuan Carlos Olamendy·Mar 8, 2024Tackling the Challenge of Imbalanced Datasets: A Comprehensive GuideHave you ever trained a model that seemed perfect during development but performed poorly in real-life scenarios, especially when dealing with rare events or classes? This is a common pitfall when working with imbalanced datasets, a prevalent issue i...9 min read·juancolamendy.hashnode.dev
AAkash Kumar·Apr 22, 2023Understanding Imbalanced Datasets and their Impact on Machine LearningImbalanced data is like a needle in a haystack, and imbalanced learning is the process of finding that needle accurately and efficiently.3 min read·akashkunwar.hashnode.dev
SSaumya Gupta·Apr 21, 2023Tackling Imbalanced Data in Machine Learning: Techniques for Accurate PredictionsExploring Undersampling, Oversampling, and Hybrid Approaches for Handling Imbalanced Data11 min read·saumyagupta.hashnode.dev
SSanjay Nandakumar·Mar 10, 2023Managing Imbalanced Data in Machine Learning: A beginner's guideA brief look into several techniques for dealing with imbalanced data while tackling classification use cases4 min read·sanjay7907.hashnode.dev
PPinak Datta·Feb 10, 2023Handling Imbalanced Data in Machine LearningIntroduction: Machine Learning has been a revolutionary technology in data analysis and prediction. However, one of the major challenges in Machine Learning is handling imbalanced data. In this article, we'll discuss the impact of imbalanced data on ...3 min read·pinakdatta.hashnode.dev
