3 ways to evaluate and improve machine learning models

When solving machine learning problems, simply training a model based on a problem-specific training machine learning algorithm does not guarantee either that the resulting model fully captures the underlying concept hidden in the training data or that the optimum parameter values were chosen for model training. Failing to test a model's performance means an underperforming model could be deployed on the production system, resulting in incorrect predictions. Choosing one model from the many available options based on intuition alone is risky. By generating different metrics, the efficacy of the model can be assessed. Use of these metrics reveals how well the model fits the data on which it was trained.