Step 5 evaluation

Model evaluation deals with how accurate or useful the model you have just developed is or will be in the future. Model evaluation can take different forms. Some are more subjective and are domain oriented, such as placing it under the scrutiny of experts in your field, and some are more technically oriented. There are many metrics and procedures available to assess a model. At the basic level, you have many statistics (some of them with acronyms known as AIC, BIC, and AUC) which purport to convey the goodness of a model in a single metric. However, these metrics by themselves are unable to convey the purpose and application of a predictive model to a larger audience and often these metrics are in conflict. Some context is needed. Some would argue that one could also develop a perfectly good predictive model and then be unable to convey its purpose and application to a larger audience. In my opinion, that is a bad model, regardless of how well an evaluation metric fits. And then there is the performance factor. A model may work well on sample data but be too slow to become actionable in the real world. In short, there is no single metric that you should use for model evaluation. The best course is to look at it from all angles and then present the objective results.