A Simple Guide to Machine Learning Visualisations - KDnuggets

An important step in developing machine learning models is to evaluate the performance. Depending on the type of machine learning problem that you are dealing with, there is generally a choice of metrics to choose from to perform this step. However, simply looking at one or two numbers in isolation cannot always enable us to make the right choice for model selection. For example, a single error metric doesn't give us any information about the distribution of the errors. It does not answer questions like is the model wrong in a big way a small number of times, or is it producing lots of smaller errors?