Achieving better performance in feature engineering
Throughout this book, we have relied on a base definition of better when it came to the various feature engineering methods we put into place. Our implicit goal was to achieve better predictive performance measured purely on simple metrics such as accuracy for classification tasks and RMSE for regression tasks (mostly accuracy). There are other metrics we may measure and track to gauge predictive performance. For example, we will use the following metrics for classification:
- True and false positive rate
- Sensitivity (AKA true positive rate) and specificity
- False negative and false positive rate
and for regression, the metrics that will be applied are:
- Mean absolute error
- R2
These lists go on, and while we will not be abandoning the idea of quantifying performance through metrics such as the ones precedingly listed, we may also measure other meta metrics, or metrics that do not directly correlate to the performance of the prediction of the model...
