Calculate Accuracy Metrics from Modeltime Resamples
Source:R/modeltime_resample_accuracy.R
modeltime_resample_accuracy.RdThis is a wrapper for yardstick that simplifies time
series regression accuracy metric calculations from
a Modeltime Table that has been resampled and fitted using
modeltime_fit_resamples().
Usage
modeltime_resample_accuracy(
object,
summary_fns = mean,
metric_set = default_forecast_accuracy_metric_set(),
...
)Arguments
- object
a Modeltime Table with a column '.resample_results' (the output of
modeltime_fit_resamples())- summary_fns
One or more functions to analyze resamples. The default is
mean(). Possible values are:NULL, to returns the resamples untransformed.
A function, e.g. mean.
A purrr-style lambda, e.g. ~ mean(.x, na.rm = TRUE)
A list of functions/lambdas, e.g. list(mean = mean, sd = sd)
- metric_set
A
yardstick::metric_set()that is used to summarize one or more forecast accuracy (regression) metrics.- ...
Additional arguments passed to the function calls in
summary_fns.
Details
#' Default Accuracy Metrics
The following accuracy metrics are included by default via modeltime::default_forecast_accuracy_metric_set():
MAE - Mean absolute error,
yardstick::mae()MAPE - Mean absolute percentage error,
yardstick::mape()MASE - Mean absolute scaled error,
yardstick::mase()SMAPE - Symmetric mean absolute percentage error,
yardstick::smape()RMSE - Root mean squared error,
yardstick::rmse()RSQ - R-squared,
yardstick::rsq()
Summary Functions
By default, modeltime_resample_accuracy() returns
the average accuracy metrics for each resample prediction.
The user can change this default behavior using summary_fns.
Simply pass one or more Summary Functions. Internally, the functions are passed to
dplyr::across(.fns), which applies the summary functions.
Returning Unsummarized Results
You can pass summary_fns = NULL to return unsummarized results by .resample_id.
Professional Tables (Interactive & Static)
Use modeltime::table_modeltime_accuracy() to format the results for reporting in
reactable (interactive) or gt (static) formats, which are perfect for
Shiny Apps (interactive) and PDF Reports (static).
Examples
library(modeltime)
# Mean (Default)
m750_training_resamples_fitted %>%
modeltime_resample_accuracy() %>%
table_modeltime_accuracy(.interactive = FALSE)
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#> <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
#> <thead>
#> <tr class="gt_heading">
#> <td colspan="10" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Accuracy Table</td>
#> </tr>
#>
#> <tr class="gt_col_headings">
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id=".model_id">.model_id</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id=".model_desc">.model_desc</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id=".type">.type</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="n">n</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="mae">mae</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="mape">mape</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="mase">mase</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="smape">smape</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="rmse">rmse</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="rsq">rsq</th>
#> </tr>
#> </thead>
#> <tbody class="gt_table_body">
#> <tr><td headers=".model_id" class="gt_row gt_right">1</td>
#> <td headers=".model_desc" class="gt_row gt_left">ARIMA(0,1,1)(0,1,1)[12]</td>
#> <td headers=".type" class="gt_row gt_left">Resamples</td>
#> <td headers="n" class="gt_row gt_right">6</td>
#> <td headers="mae" class="gt_row gt_right">294.51</td>
#> <td headers="mape" class="gt_row gt_right">2.95</td>
#> <td headers="mase" class="gt_row gt_right">1.18</td>
#> <td headers="smape" class="gt_row gt_right">2.94</td>
#> <td headers="rmse" class="gt_row gt_right">355.49</td>
#> <td headers="rsq" class="gt_row gt_right">0.80</td></tr>
#> <tr><td headers=".model_id" class="gt_row gt_right">2</td>
#> <td headers=".model_desc" class="gt_row gt_left">PROPHET</td>
#> <td headers=".type" class="gt_row gt_left">Resamples</td>
#> <td headers="n" class="gt_row gt_right">6</td>
#> <td headers="mae" class="gt_row gt_right">458.05</td>
#> <td headers="mape" class="gt_row gt_right">4.59</td>
#> <td headers="mase" class="gt_row gt_right">1.85</td>
#> <td headers="smape" class="gt_row gt_right">4.55</td>
#> <td headers="rmse" class="gt_row gt_right">515.49</td>
#> <td headers="rsq" class="gt_row gt_right">0.78</td></tr>
#> <tr><td headers=".model_id" class="gt_row gt_right">3</td>
#> <td headers=".model_desc" class="gt_row gt_left">GLMNET</td>
#> <td headers=".type" class="gt_row gt_left">Resamples</td>
#> <td headers="n" class="gt_row gt_right">6</td>
#> <td headers="mae" class="gt_row gt_right">706.10</td>
#> <td headers="mape" class="gt_row gt_right">7.16</td>
#> <td headers="mase" class="gt_row gt_right">2.80</td>
#> <td headers="smape" class="gt_row gt_right">6.92</td>
#> <td headers="rmse" class="gt_row gt_right">753.04</td>
#> <td headers="rsq" class="gt_row gt_right">0.77</td></tr>
#> </tbody>
#>
#>
#> </table>
#> </div>
# Mean and Standard Deviation
m750_training_resamples_fitted %>%
modeltime_resample_accuracy(
summary_fns = list(mean = mean, sd = sd)
) %>%
table_modeltime_accuracy(.interactive = FALSE)
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#> <thead>
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#> <td colspan="16" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Accuracy Table</td>
#> </tr>
#>
#> <tr class="gt_col_headings">
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id=".model_id">.model_id</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id=".model_desc">.model_desc</th>
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#> <tbody class="gt_table_body">
#> <tr><td headers=".model_id" class="gt_row gt_right">1</td>
#> <td headers=".model_desc" class="gt_row gt_left">ARIMA(0,1,1)(0,1,1)[12]</td>
#> <td headers=".type" class="gt_row gt_left">Resamples</td>
#> <td headers="n" class="gt_row gt_right">6</td>
#> <td headers="mae_mean" class="gt_row gt_right">294.51</td>
#> <td headers="mae_sd" class="gt_row gt_right">121.98</td>
#> <td headers="mape_mean" class="gt_row gt_right">2.95</td>
#> <td headers="mape_sd" class="gt_row gt_right">1.27</td>
#> <td headers="mase_mean" class="gt_row gt_right">1.18</td>
#> <td headers="mase_sd" class="gt_row gt_right">0.56</td>
#> <td headers="smape_mean" class="gt_row gt_right">2.94</td>
#> <td headers="smape_sd" class="gt_row gt_right">1.24</td>
#> <td headers="rmse_mean" class="gt_row gt_right">355.49</td>
#> <td headers="rmse_sd" class="gt_row gt_right">138.68</td>
#> <td headers="rsq_mean" class="gt_row gt_right">0.80</td>
#> <td headers="rsq_sd" class="gt_row gt_right">0.15</td></tr>
#> <tr><td headers=".model_id" class="gt_row gt_right">2</td>
#> <td headers=".model_desc" class="gt_row gt_left">PROPHET</td>
#> <td headers=".type" class="gt_row gt_left">Resamples</td>
#> <td headers="n" class="gt_row gt_right">6</td>
#> <td headers="mae_mean" class="gt_row gt_right">458.05</td>
#> <td headers="mae_sd" class="gt_row gt_right">196.09</td>
#> <td headers="mape_mean" class="gt_row gt_right">4.59</td>
#> <td headers="mape_sd" class="gt_row gt_right">1.98</td>
#> <td headers="mase_mean" class="gt_row gt_right">1.85</td>
#> <td headers="mase_sd" class="gt_row gt_right">0.91</td>
#> <td headers="smape_mean" class="gt_row gt_right">4.55</td>
#> <td headers="smape_sd" class="gt_row gt_right">1.86</td>
#> <td headers="rmse_mean" class="gt_row gt_right">515.49</td>
#> <td headers="rmse_sd" class="gt_row gt_right">175.26</td>
#> <td headers="rsq_mean" class="gt_row gt_right">0.78</td>
#> <td headers="rsq_sd" class="gt_row gt_right">0.09</td></tr>
#> <tr><td headers=".model_id" class="gt_row gt_right">3</td>
#> <td headers=".model_desc" class="gt_row gt_left">GLMNET</td>
#> <td headers=".type" class="gt_row gt_left">Resamples</td>
#> <td headers="n" class="gt_row gt_right">6</td>
#> <td headers="mae_mean" class="gt_row gt_right">706.10</td>
#> <td headers="mae_sd" class="gt_row gt_right">248.29</td>
#> <td headers="mape_mean" class="gt_row gt_right">7.16</td>
#> <td headers="mape_sd" class="gt_row gt_right">2.62</td>
#> <td headers="mase_mean" class="gt_row gt_right">2.80</td>
#> <td headers="mase_sd" class="gt_row gt_right">1.11</td>
#> <td headers="smape_mean" class="gt_row gt_right">6.92</td>
#> <td headers="smape_sd" class="gt_row gt_right">2.41</td>
#> <td headers="rmse_mean" class="gt_row gt_right">753.04</td>
#> <td headers="rmse_sd" class="gt_row gt_right">252.75</td>
#> <td headers="rsq_mean" class="gt_row gt_right">0.77</td>
#> <td headers="rsq_sd" class="gt_row gt_right">0.16</td></tr>
#> </tbody>
#>
#>
#> </table>
#> </div>
# When summary_fns = NULL, returns the unsummarized resample results
m750_training_resamples_fitted %>%
modeltime_resample_accuracy(
summary_fns = NULL
)
#> # A tibble: 18 × 10
#> .model_id .model_desc .resample_id .type mae mape mase smape rmse rsq
#> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 ARIMA(0,1,1… Slice1 Resa… 182. 1.79 0.637 1.80 212. 0.950
#> 2 1 ARIMA(0,1,1… Slice2 Resa… 330. 3.24 1.30 3.17 406. 0.729
#> 3 1 ARIMA(0,1,1… Slice3 Resa… 359. 3.57 1.55 3.64 415. 0.859
#> 4 1 ARIMA(0,1,1… Slice4 Resa… 456. 4.76 2.01 4.61 530. 0.561
#> 5 1 ARIMA(0,1,1… Slice5 Resa… 122. 1.23 0.524 1.22 165. 0.926
#> 6 1 ARIMA(0,1,1… Slice6 Resa… 317. 3.12 1.05 3.20 407. 0.763
#> 7 2 PROPHET Slice1 Resa… 450. 4.54 1.57 4.40 509. 0.869
#> 8 2 PROPHET Slice2 Resa… 449. 4.34 1.76 4.45 494. 0.745
#> 9 2 PROPHET Slice3 Resa… 475. 4.72 2.05 4.85 556. 0.660
#> 10 2 PROPHET Slice4 Resa… 317. 3.33 1.39 3.26 362. 0.781
#> 11 2 PROPHET Slice5 Resa… 813. 8.22 3.49 7.89 829. 0.897
#> 12 2 PROPHET Slice6 Resa… 244. 2.40 0.808 2.45 344. 0.755
#> 13 3 GLMNET Slice1 Resa… 893. 8.90 3.12 8.48 926. 0.845
#> 14 3 GLMNET Slice2 Resa… 583. 5.73 2.29 5.53 647. 0.654
#> 15 3 GLMNET Slice3 Resa… 874. 9.07 3.77 8.62 917. 0.901
#> 16 3 GLMNET Slice4 Resa… 964. 10.1 4.24 9.53 1022. 0.494
#> 17 3 GLMNET Slice5 Resa… 313. 3.17 1.34 3.11 339. 0.918
#> 18 3 GLMNET Slice6 Resa… 609. 6.03 2.02 6.26 667. 0.783