using yardstick (or other package) for performance calculations

The loss functions included are great but are somewhat limited.

There are a lot of R packages that could expand the types of loss functions (but at the loss of R:python parity).

If you were to use yardstick, for example, the benefits would be:

• More metrics
• Data on direction (e.g larger-is-better) for each metrics. You wouldn't have to do 1 - AUC anymore.
• yardstick can compute multiple metrics at once.
• Numerous multi-class metrics
• Metrics for censored regression
• Extensible for user-defined metrics

The downside to the current system is that you might optimize your model on a set of performance scores and judge feature importance on some other score.

Let use know if we can help or put in a PR.

Review the existing loss-function implementation and compare its coverage with yardstick's metrics, direction metadata, multi-metric support, multiclass metrics, censored regression, and user-defined metrics. The issue names no files, tests, entry points, or completion criteria, so confirm the intended scope and R/Python parity requirements with maintainers before coding.