Evaluation of Predictive Modelling

We can only succeed in capturing insights from data if we first know how to measure the effectiveness of models. In this course, you will learn appropriate measures that are used to evaluate predictive models in a variety of contexts. You will also learn about procedures that are used to ensure that models do not cheat through, for example, overfitting or predicting incorrect distributions.  A predictive exercise is not finished when a model is built. It is important to construct a vast number of models, not only to find the best one, but to ensure that they point in the same direction. This course will equip you with essential skills and knowledge for understanding performance evaluation metrics, to determine whether a model is performing adequately or not.   You will also discover the ways that different model evaluation criteria illustrate how one model excels over another, and how to identify when to use certain criteria over others. This is the foundation to performing successful predictive analysis.

Created by: The University of Edinburgh

Level: Advanced

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