Assessing the value of a ML model is a two-phase process. First, the model has to be evaluated for its statistical accuracy, that is, whether the statistical hypotheses are correct, model performance is outstanding, and the performance holds true for other independent datasets. This is accomplished using several model evaluation metrics. Then, a model is evaluated to see if the results are as expected as per business requirement and the stakeholders genuinely get some insights or useful predictions out of it.
A regression model is evaluated based on the following metrics:
- Mean absolute error (MAE): It is the sum of absolute values of prediction error. The prediction error is defined as the difference between predicted and actual values. This metric gives an idea about the magnitude of the error. However, we cannot judge...
