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Data imbalance is a common problem in machine learning classification where the training dataset contains a disproportionate ratio of samples in each class. Examples of real-world scenarios that suffer from class imbalance include threat detection, medical diagnosis, and spam filtering. At Modzy, we’re conscious of this challenge and have procedures built into our model training processes to minimize the impact of data imbalance. There are many techniques for handling class imbalance during training such as using a data-driven approach (resampling) or an algorithmic approach (ensemble models)