Multiclass Probability Predictions
Kuhn, M., Johnson, K. (2013) Applied Predictive Modeling, Springer
a data frame
This data frame contains the predicted classes and
class probabilities for a linear discriminant analysis model fit
to the HPC data set from Kuhn and Johnson (2013). These data are
the assessment sets from a 10-fold cross-validation scheme. The
data column columns for the true class (obs), the class
prediction (pred) and columns for each class probability
(columns VF, F, M, and L). Additionally, a column for
the resample indicator is included.
#> 'data.frame': 3467 obs. of 7 variables: #> $ obs : Factor w/ 4 levels "VF","F","M","L": 1 1 1 1 1 1 1 1 1 1 ... #> $ pred : Factor w/ 4 levels "VF","F","M","L": 1 1 1 1 1 1 1 1 1 1 ... #> $ VF : num 0.914 0.938 0.947 0.929 0.942 ... #> $ F : num 0.0779 0.0571 0.0495 0.0653 0.0543 ... #> $ M : num 0.00848 0.00482 0.00316 0.00579 0.00381 ... #> $ L : num 1.99e-05 1.01e-05 5.00e-06 1.56e-05 7.29e-06 ... #> $ Resample: chr "Fold01" "Fold01" "Fold01" "Fold01" ...# `obs` is a 4 level factor. The first level is `"VF"`, which is the # "event of interest" by default in yardstick. See the Relevant Level # section in any classification function (such as `?pr_auc`) to see how # to change this. levels(hpc_cv$obs)#> [1] "VF" "F" "M" "L"