Example Data Sets

## 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 × 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 Fold… 0.722 0.328 0.798 0.729 0.720 0.489
#> 2 NA Fold… 0.696 0.290 0.778 0.720 0.691 0.456
#> 3 NA Fold… 0.701 0.297 0.790 0.723 0.696 0.486
#> 4 NA Fold… 0.704 0.316 0.795 0.763 0.691 0.497
#> 5 NA Fold… 0.721 0.324 0.797 0.722 0.721 0.481
#> 6 NA Fold… 0.711 0.303 0.780 0.706 0.712 0.484
#> 7 NA Fold… 0.702 0.305 0.790 0.739 0.694 0.485
#> 8 NA Fold… 0.718 0.321 0.784 0.729 0.715 0.477
#> 9 NA Fold… 0.720 0.328 0.795 0.739 0.715 0.491
#> 10 NA Fold… 0.719 0.324 0.796 0.728 0.717 0.488
#> # ℹ 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>
```