Machine Learning for Imbalanced Data

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Ensemble Methods

Think of a top executive at a major company. They don’t make decisions on their own. Throughout the day, they need to make numerous critical decisions. How do they make those choices? Not alone, but by consulting their advisors.

Let’s say that an executive consults five different advisors from different departments, each proposing a slightly different solution based on their expertise, skills, and domain knowledge. To make the most effective decision, the executive combines the insights and opinions of all five advisors to create a hybrid solution that incorporates the best parts of each proposal. This scenario illustrates the concept of ensemble methods, where multiple weak classifiers are combined to create a stronger and ...