Imbalanced Data
Does Undersampling Help with Imbalanced Data?
Undersampling is one of the most popular fixes for imbalanced data. I review the evidence...
What Are the Best Classifiers for Imbalanced Data?
Which classifiers work best for imbalanced data? I compared gradient boosting, random forests, logistic regression,...
Should You Use Imbalanced-Learn in 2026?
I discuss the latest evidence on undersampling, SMOTE and resampling ensembles for imbalanced data, and...
Exploring Oversampling Techniques for Imbalanced Datasets
Oversampling techniques for imbalanced datasets in Python, and what the latest evidence says about whether...
Confusion Matrix, Precision, and Recall: A Complete Guide With Python Examples
What is a confusion matrix, and how do we get precision and recall from it?...
Isotonic Regression: A Complete Guide With Python Examples
Isotonic regression fits the best monotonic function to your data. Learn how it works, how...
Cost-Sensitive Learning: Beyond the Accuracy in Imbalanced Classification
Cost-sensitive learning is threshold shifting. Find out when class weights make sense, when to adjust...
Class Imbalance in Machine Learning: Definition, Imbalance Ratio and Why It's Rarely the Problem
Class imbalance is rarely the real problem. Learn what it is, what really makes imbalanced...
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...
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...