In many applications (e.g. medical data or fraud detection) it is common to have imbalanced data: the cases that you are mainly interested in only occur infrequently. This video covers techniques to correct this imbalance to build better models. These include random over- and undersampling, synthetic data generation, and balanced ensembles. We will also compare these techniques on real-world imbalanced datasets and explain how to use them in practice.
