Python Machine Learning Blueprints - Second Edition

Evaluation

So, now you've got a shiny new model, but exactly how good is that model? This is the question that the evaluation phase seeks to answer. There are a number of ways to measure the performance of a model, and again it is largely dependent on the type of data you are working with and the type of model used, but on the whole, we are seeking to answer the question of how close the model's predictions are to the actual value. There is an array of confusing sounding terms, such as root mean-square error, or Euclidean distance, or F1 score. But in the end, they are all just a measure of distance between the actual prediction and the estimated prediction.