Evaluating predictive accuracy using cross-validation
set.seed(84735)
prediction_summary_cv(model = weather_model_1,
data = weather_WU, k = 10)
## $folds
## fold mae mae_scaled within_50 within_95
## 1 1 3.564498 0.8445706 0.30 1.00
## 2 2 2.725559 0.6619840 0.55 0.90
## 3 3 3.023616 0.7514263 0.40 0.85
## 4 4 3.199433 0.7578761 0.50 1.00
## 5 5 3.790749 0.9170231 0.25 1.00
## 6 6 3.372913 0.7968418 0.45 1.00
## 7 7 2.880373 0.6953989 0.50 0.95
## 8 8 2.840581 0.6738942 0.50 1.00
## 9 9 4.096178 0.9900088 0.30 1.00
## 10 10 3.355105 0.8062387 0.30 1.00
##
## $cv
## mae mae_scaled within_50 within_95
## 1 3.284901 0.7895262 0.405 0.97