Multiclass Probability Predictions

## Source

Kuhn, M., Johnson, K. (2013) Applied Predictive Modeling, Springer

hpc_cv

a data frame

## Details

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.

## Examples

data(hpc_cv)
str(hpc_cv)#> '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"