Evaluating machine learning models: How to tackle metrics - codecentric AG Blog

Once a model has been trained, it can be evaluated in different ways and with more or less complex and meaningful procedures and metrics. However, the number and possible criteria for evaluating machine learning models can initially be quite confusing to someone who is just starting to deal with the field of machine learning. For example, it depends on whether the learning is un-supervised or supervised. In the case of supervised learning it also depends on whether we are dealing with a regression or classification, the underlying use case, and so on – to name just a few criteria. I would like to start with supervised learning and classification.