After you develop a machine learning model for your predictive modeling problem, how do you know if the performance of the model is any good?This is a common question I am asked by beginners.
As a beginner, you often seek an answer to this question, e.g. you want someone to tell you whether an accuracy of x% or an error score of x is good or not.
In this post, you will discover how to answer this question for yourself definitively and know whether your model skill is good or not.After reading this post, you will know:
- That a baseline model can be used to discover the bedrock in performance on your problem by which all other models can be evaluated.
- That all predictive models contain errors and that a perfect score is not possible in practice given the stochastic nature of data and algorithms.
- That the true job of applied machine learning is to explore the space of possible models and discover what a good model score looks like relative to the baseline on your specific dataset.
Let’s get started.
