8.3 Error metrics

\[ \begin{aligned} \text{MAE} &= \dfrac{1}{n}\sum_{i=1}^{n} \left| \hat{y}_i - y_i \right|\\ \text{RMSE} &= \sqrt{\dfrac{1}{n}\sum_{i=1}^{n} \left( \hat{y}_i - y_i \right)^2} \end{aligned} \]

Regression Metrics Guide: https://www.h2o.ai/blog/regression-metrics-guide/

library(yardstick)
## For binary classification, the first factor level is assumed to be the event.
## Set the global option `yardstick.event_first` to `FALSE` to change this.

yardstick::rmse(price_pred, truth = truth, estimate = .pred)
## # A tibble: 1 x 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 rmse    standard      56505.