## 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.
library(yardstick)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## truth Class1 Class2 predicted
## 1 Class2 0.003589243 0.9964107574 Class2
## 2 Class1 0.678621054 0.3213789460 Class1
## 3 Class2 0.110893522 0.8891064779 Class2
## 4 Class1 0.735161703 0.2648382969 Class1
## 5 Class2 0.016239960 0.9837600397 Class2
## 6 Class1 0.999275071 0.0007249286 Class1
metrics(two_class_example, truth, predicted)
## # A tibble: 2 x 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 accuracy binary 0.838
## 2 kap binary 0.675
two_class_example %>%
roc_auc(truth, Class1)
## # A tibble: 1 x 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 roc_auc binary 0.939
data("hpc_cv")
hpc_cv <- as_tibble(hpc_cv)
hpc_cv
## # A tibble: 3,467 x 7
## obs pred VF F M L Resample
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 VF VF 0.914 0.0779 0.00848 0.0000199 Fold01
## 2 VF VF 0.938 0.0571 0.00482 0.0000101 Fold01
## 3 VF VF 0.947 0.0495 0.00316 0.00000500 Fold01
## 4 VF VF 0.929 0.0653 0.00579 0.0000156 Fold01
## 5 VF VF 0.942 0.0543 0.00381 0.00000729 Fold01
## 6 VF VF 0.951 0.0462 0.00272 0.00000384 Fold01
## 7 VF VF 0.914 0.0782 0.00767 0.0000354 Fold01
## 8 VF VF 0.918 0.0744 0.00726 0.0000157 Fold01
## 9 VF VF 0.843 0.128 0.0296 0.000192 Fold01
## 10 VF VF 0.920 0.0728 0.00703 0.0000147 Fold01
## # ... with 3,457 more rows
precision(hpc_cv, obs, pred)
## # A tibble: 1 x 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 precision macro 0.631
precision(hpc_cv, obs, pred, estimator = "micro")
## # A tibble: 1 x 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 precision micro 0.709
hpc_cv %>%
group_by(Resample) %>%
roc_auc(obs, VF:L)
## # A tibble: 10 x 4
## Resample .metric .estimator .estimate
## <chr> <chr> <chr> <dbl>
## 1 Fold01 roc_auc hand_till 0.831
## 2 Fold02 roc_auc hand_till 0.817
## 3 Fold03 roc_auc hand_till 0.869
## 4 Fold04 roc_auc hand_till 0.849
## 5 Fold05 roc_auc hand_till 0.811
## 6 Fold06 roc_auc hand_till 0.836
## 7 Fold07 roc_auc hand_till 0.825
## 8 Fold08 roc_auc hand_till 0.846
## 9 Fold09 roc_auc hand_till 0.836
## 10 Fold10 roc_auc hand_till 0.820
library(ggplot2)
hpc_cv %>%
group_by(Resample) %>%
roc_curve(obs, VF:L) %>%
autoplot()
