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Machine Learning for OpenCV
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Model evaluation strategies come in many different forms and shapes. In the following sections, we will, therefore, highlight three of the most commonly used techniques to compare models against each other:
In principle, model evaluation is simple: after training a model on some data, we can estimate its effectiveness by comparing model predictions to some ground truth values. We learned early on that we should split the data into a training and a test set, and we tried to follow this instruction whenever possible. But why exactly did we do that again?
The reason we never evaluate a model on the training set is that, in principle, any dataset can be learned if we throw a strong enough model at it.
A quick demonstration of this can be given with help of the Iris dataset, which we talked about extensively in Chapter 3, First Steps in Supervised Learning. There the goal was to classify species...