Example Data Sets

## Value

Tibbles with the additional class rset

## Details

Several data sets are contained in the package as examples. Each simulates an rset object but the splits columns are not included to save space.

• precise_example contains the results of the classification analysis of a real data set using 10-fold CV. The holdout data sets contained thousands of examples and have precise performance estimates. Three models were fit to the original data and several performance metrics are included.

• noisy_example was also generated from a regression data simulation. The original data set was small (50 samples) and 10-repeated of 10-fold CV were used with four models. There is an excessive of variability in the results (probably more than the resample-to-resample variability). The RMSE distributions show fairly right-skewed distributions.

• concrete_example contains the results of the regression case study from the book Applied Predictive Modeling. The original data set contained 745 samples in the training set. 10-repeats of 10-fold CV was also used and 13 models were fit to the data.

• ts_example is from a data set where rolling-origin forecast resampling was used. Each assessment set is the summary of 14 observations (i.e. 2 weeks). The analysis set consisted of a base of about 5,500 samples plus the previous assessment sets. Four regression models were applied to these data.

• ex_object objects were generated from the two_class_dat data in the modeldata package. Basic 10-fold cross validation was used to evaluate the models. The posterior_samples object is samples of the posterior distribution of the model ROC values while contrast_samples are posterior probabilities form the differences in ROC values.

## Examples

data(precise_example)
precise_example
#> #  10-fold cross-validation using stratification
#> # A tibble: 10 x 29
#>    splits id     glm_Accuracy glm_Kappa glm_ROC glm_Sens glm_Spec glm_PRAUC
#>    <lgl>  <chr>         <dbl>     <dbl>   <dbl>    <dbl>    <dbl>     <dbl>
#>  1 NA     Fold01        0.722     0.328   0.798    0.729    0.720     0.489
#>  2 NA     Fold02        0.696     0.290   0.778    0.720    0.691     0.456
#>  3 NA     Fold03        0.701     0.297   0.790    0.723    0.696     0.486
#>  4 NA     Fold04        0.704     0.316   0.795    0.763    0.691     0.497
#>  5 NA     Fold05        0.721     0.324   0.797    0.722    0.721     0.481
#>  6 NA     Fold06        0.711     0.303   0.780    0.706    0.712     0.484
#>  7 NA     Fold07        0.702     0.305   0.790    0.739    0.694     0.485
#>  8 NA     Fold08        0.718     0.321   0.784    0.729    0.715     0.477
#>  9 NA     Fold09        0.720     0.328   0.795    0.739    0.715     0.491
#> 10 NA     Fold10        0.719     0.324   0.796    0.728    0.717     0.488
#> # … with 21 more variables: glm_Precision <dbl>, glm_Recall <dbl>, glm_F <dbl>,
#> #   knn_Accuracy <dbl>, knn_Kappa <dbl>, knn_ROC <dbl>, knn_Sens <dbl>,
#> #   knn_Spec <dbl>, knn_PRAUC <dbl>, knn_Precision <dbl>, knn_Recall <dbl>,
#> #   knn_F <dbl>, nnet_Accuracy <dbl>, nnet_Kappa <dbl>, nnet_ROC <dbl>,
#> #   nnet_Sens <dbl>, nnet_Spec <dbl>, nnet_PRAUC <dbl>, nnet_Precision <dbl>,
#> #   nnet_Recall <dbl>, nnet_F <dbl>