Master techniques for handling data imbalance in machine learning. Progress from data preparation and baseline modeling to advanced resampling, evaluation metrics, and specialized algorithms for imbalanced datasets to build robust, fair models.
In this course, you'll go through the essential steps for loading, exploring, and preprocessing the data. You'll handle missing values, encode categorical variables, and train a baseline model to establish a performance benchmark.
Evaluation Metrics & Advanced Techniques for Imbalanced Data
4 lessons
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14 practices
In this course, you'll learn to recognize and address class imbalance in datasets. Explore practical undersampling and oversampling techniques, visualize their effects, and apply advanced resampling strategies. By the end, you'll be able to train models that perform better on imbalanced data.
This course focuses on evaluating models with imbalanced data, and explores advanced techniques including cost-sensitive learning, ensemble methods, and anomaly detection for extreme imbalance. You'll learn appropriate evaluation strategies specifically designed for imbalanced datasets, helping you choose the right metrics and avoid common pitfalls.
