Imbalanced classification with python

Classification predictive modeling involves assigning a class label to an example. It may be one of the most studied and used areas of machine learning. Nevertheless, the majority of the models used to learn from Classification data and the metrics used to evaluate those models assume that the distribution of the examples across the class labels is equal. The field is focused on the simplest form of Classification problems, so-called balanced Classification problems. When the examples across classes are imbalanced, many machine learning algorithms fail and metrics used to evaluate those models, such as Classification accuracy, become dangerously misleading. Many problems have a skew in the class distribution. For example, numerous tasks where practitioners want to apply machine learning are examples of imbalanced Classification, including fraud detection, churn prediction, medical diagnosis, and many more. In fact, it may be more common to have imbalanced classes than balanced classes. Thankfully, there is a small but rapidly growing field of study dedicated to the problem of imbalanced Classification. This field includes modifications to existing algorithms to make them useful for imbalanced Classification, careful selection of performance metrics, and entirely new data preparation techniques and modeling algorithms. This book was carefully designed to help you bring the tools and techniques of imbalanced Classification to your next project. The tutorials were designed to teach you these techniques the fastest and most effective way that I know how: to learn by doing, with executable code that you can run to develop the intuitions required and that you can copy-and-paste into your project and immediately get a result. Imbalanced Classification is important to machine learning, and I believe that if it is taught at the right level for practitioners, it can be a fascinating, fun, directly applicable, and an immeasurably useful toolbox of techniques. I hope that you agree.