ROC AUC vs Precision-Recall for Imbalanced Data

When building machine learning models to classify imbalanced data — i.

Tips for Handling Imbalanced Data in Machine Learning

Introduction Imperfect data is the norm rather than the exception in machine learning. Comparably common is the binary class imbalance when the classes in a trained data remains majority/minority class, or is moderately skewed. Imbalanced data can undermine a machine learning model by producing model selection biases. Therefore in the…

May 25, 2024

In "AI/ML Research"

Navigating Imbalanced Datasets with Pandas and Scikit-learn

Imbalanced datasets, where a majority of the data samples belong to one class and the remaining minority belong to others, are not that rare.

June 13, 2025

In "AI/ML Research"

5 Effective Ways to Handle Imbalanced Data in Machine Learning

Introduction Here’s a something that new machine learning practitioners figure out almost immediately: not all datasets are created equal. It may now seem obvious to you, but had you considered this before undertaking machine learning projects on a real world dataset? As an example of a single class vastly outnumbering…

June 26, 2024

In "AI/ML Research"

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