Imbalanced Data
Precision-Recall Curves: A Complete Guide With Python Examples
What is a Precision-Recall curve, how does it differ from a ROC curve, and when...
Imbalanced Data in Machine Learning: Techniques and Best Practices
Imbalanced data isn't the problem. How you handle it is. Learn how to work with...
SMOTE in Python and whether you should still use it in 2026
Learn how to implement SMOTE in Python and whether you should still use it to...
Should You Use Imbalanced-Learn in 2025?
I discuss the latest evidence on the use of undersampling and SMOTE for imbalanced data...
ADASYN: Adaptive Synthetic Sampling for Imbalanced Datasets
Find out why you should NOT use ADASYN to handle data imbalance, what the hype...
The Complete Guide to Platt Scaling
Learn about calibration in machine learning using Platt scaling. Find out how it works and...
ROC-AUC Analysis – A Deep Dive
Ultimate guide for mastering ROC-AUC analysis—learn to create, interpret, and apply it in Python with...
Probability Calibration in Machine Learning: Enhancing Model Usability
Learn probability calibration in machine learning: importance, methods, and best practices for more reliable probability...
A Data Scientist’s Guide to Balanced Accuracy
Discover the balanced accuracy's advantages over traditional accuracy and learn how to implement it in...
Class Imbalance in Machine Learning
Contrary to what you'll read online or get from ChatGPT, class imbalance is NOT the...
Cost-Sensitive Learning: Beyond the Accuracy in Imbalanced Classification
Find out what cost-sensitive learning is and how to implement it with Python.
The Role of Undersampling in Tackling Imbalanced Datasets in Machine Learning
Undersampling techniques for imbalanced datasets in Python.
Exploring Oversampling Techniques for Imbalanced Datasets
Oversampling techniques for imbalanced datasets in Python.