Feature Engineering Made Easy

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 ...