Evaluating classifier performance on Diplodia sapinea
Patrick Schratz, Friedrich-Schiller-University Jena
Last updated: 2019-05-28
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Knit directory: 2018-model-comparison/
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Introduction
This document shows the predictive performances for the possible infection risk of trees in the Basque Country by the pathogen Diplodia sapinea.
The following algorithms were benchmarked:
- Boosted Regression Trees (BRT)
- Generalized Additive Model (GAM)
- Generalized Linear Model (GLM)
- k-Nearest Neighbor (KNN)
- Random Forests (RF)
- Support Vector Machine (SVM)
- Extreme Gradient Boosting (XGBOOST)
Resampling Strategies
The abbreviations of the tabbed resampling strategies follow the scheme:
<outer resampling> / <inner resampling>
For example, setting “Spatial-Spatial” means that in both levels a “spatial cross-validation” (Brenning (2012)) has been applied.
The inner resampling refers to the hyperparameter tuning level of the nested cross-validation that was applied.
Results structure
Teh results are structured as follows:
- Results table of all performances for each resampling setting
- Boxplot comparison across all algorithms
- Aggregated performances across all algorithms
Even though GBM shows the best performance here, RF was the winning model as long as predictor pH was included in the model. This predictor was removed after causing block artifacts in the prediction maps. The following results do not include predictor pH.
# A tibble: 922 x 13
diplo01 temp precip hail_probability ph soil lithology
<fct> <dbl> <dbl> <dbl> <dbl> <fct> <fct>
1 0 16.1 249. 0.694 4.06 soil… clastic …
2 0 16.0 237. 0.646 4.06 soil… clastic …
3 0 16.0 237. 0.646 4.06 soil… clastic …
4 0 12.6 152. 0.0238 4.96 soil… chemical…
5 0 13.8 163. 0.0996 4.96 soil… clastic …
6 0 13.7 149. 0.0532 5.14 soil… clastic …
7 0 13.7 146. 0.0637 5.14 soil… clastic …
8 0 13.8 146. 0.0743 5.14 soil… clastic …
9 0 13.7 144. 0.0901 5.14 soil… clastic …
10 0 13.9 147. 0.0692 4.98 soil… clastic …
# … with 912 more rows, and 6 more variables: slope_degrees <dbl>,
# pisr <dbl>, x <dbl>, y <dbl>, year <fct>, age <dbl>Performance results
Spatial-Spatial
task.id learner.id brier.test.mean timetrain.test.mean
1 diplodia classif.svm.tuned 0.2000721 194.6988
2 diplodia classif.gam.tuned 0.1899124 230.0244
3 diplodia classif.kknn.tuned 0.1877918 109.7034
4 diplodia classif.ranger.tuned 0.1594762 240.8807
5 diplodia classif.gbm.tuned 0.1590360 1820.5990
Spatial-Non-Spatial
task.id learner.id brier.test.mean timetrain.test.mean
1 diplodia classif.svm.tuned 0.2125015 389.3593
2 diplodia classif.gam.tuned 0.1922458 294.0095
3 diplodia classif.kknn.tuned 0.1882630 121.3932
4 diplodia classif.gbm.tuned 0.1637896 2095.5327
5 diplodia classif.ranger.tuned 0.1528584 445.1206
Non-Spatial-Non-Spatial
task.id learner.id brier.test.mean timetrain.test.mean
1 diplodia classif.gam.tuned 0.1395204 290.8944
2 diplodia classif.svm.tuned 0.1229645 406.3464
3 diplodia classif.kknn.tuned 0.1201311 121.3484
4 diplodia classif.gbm.tuned 0.1118638 1999.3005
5 diplodia classif.ranger.tuned 0.0995260 440.4030
Non-Spatial-No Tuning
task.id learner.id brier.test.mean timetrain.test.mean
1 diplodia classif.svm 0.1603230 0.638496
2 diplodia classif.gam 0.1467264 0.615932
3 diplodia classif.kknn 0.1195669 0.000772
4 diplodia classif.binomial 0.1186126 0.021954
5 diplodia classif.gbm 0.1137512 0.060954
6 diplodia classif.ranger 0.1017046 0.667556
Spatial-No Tuning
task.id learner.id brier.test.mean timetrain.test.mean
1 diplodia classif.gam 0.2505253 0.569212
2 diplodia classif.svm 0.2009484 0.627202
3 diplodia classif.binomial 0.1703007 0.022436
4 diplodia classif.kknn 0.1689237 0.001134
5 diplodia classif.gbm 0.1598117 0.062854
6 diplodia classif.ranger 0.1499009 0.651948
References
Brenning, A. 2012. “Spatial Cross-Validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing: The R Package Sperrorest.” In 2012 Ieee International Geoscience and Remote Sensing Symposium, 5372–5. https://doi.org/10.1109/IGARSS.2012.6352393.
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-7.3.0/r-3.5.1-b4xhm3pook4yl4olk6ttnovnyttdpkhe/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-7.3.0/r-3.5.1-b4xhm3pook4yl4olk6ttnovnyttdpkhe/rlib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyselect_0.2.5 workflowr_1.3.0 here_0.1
[4] kableExtra_1.1.0 ggExtra_0.8 ggrepel_0.8.0
[7] reporttools_1.1.2 xtable_1.8-3 cowplot_0.9.3
[10] hrbrthemes_0.6.0 ggpubr_0.2 future.callr_0.4.0
[13] furrr_0.1.0.9002 future_1.11.1.1 ggsci_2.9
[16] clustermq_0.8.6 ggspatial_1.0.3 ggplot2_3.0.0
[19] rgenoud_5.8-3.0 fs_1.2.6 curl_3.2
[22] R.utils_2.7.0 R.oo_1.22.0 R.methodsS3_1.7.1
[25] GSIF_0.5-5 stringr_1.3.1 RSAGA_1.3.0
[28] plyr_1.8.4 shapefiles_0.7 foreign_0.8-71
[31] gstat_1.1-6 glue_1.3.0 rasterVis_0.45
[34] latticeExtra_0.6-28 RColorBrewer_1.1-2 lattice_0.20-35
[37] raster_2.8-19 viridis_0.5.1 viridisLite_0.3.0
[40] rgdal_1.4-3 sp_1.3-1 tibble_2.0.1
[43] forcats_0.3.0 lwgeom_0.1-6 dplyr_0.8.0.1
[46] sf_0.7-4 parallelMap_1.3 purrr_0.2.5
[49] mlrMBO_1.1.2 smoof_1.5.1 checkmate_1.8.5
[52] BBmisc_1.11 magrittr_1.5 mlr_2.13.9000
[55] ParamHelpers_1.11 drake_7.3.0.9000
loaded via a namespace (and not attached):
[1] backports_1.1.2 Hmisc_4.2-0 fastmatch_1.1-0
[4] igraph_1.2.2 lazyeval_0.2.1 splines_3.5.1
[7] storr_1.2.1 listenv_0.7.0 digest_0.6.15
[10] htmltools_0.3.6 fansi_0.4.0 base64url_1.4
[13] cluster_2.0.7-1 readr_1.3.1 globals_0.12.4
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[19] colorspace_1.3-2 rvest_0.3.2 pixmap_0.4-11
[22] xfun_0.7 callr_3.1.0 crayon_1.3.4
[25] jsonlite_1.5 hexbin_1.27.2 survival_2.42-3
[28] zoo_1.8-3 gtable_0.2.0 webshot_0.5.1
[31] Rttf2pt1_1.3.7 scales_1.0.0 DBI_1.0.0
[34] miniUI_0.1.1.1 Rcpp_1.0.0 plotrix_3.7-4
[37] spData_0.2.9.0 htmlTable_1.12 units_0.6-2
[40] Formula_1.2-3 intervals_0.15.1 dismo_1.1-4
[43] htmlwidgets_1.3 httr_1.3.1 FNN_1.1
[46] aqp_1.17 acepack_1.4.1 pkgconfig_2.0.2
[49] reshape_0.8.8 XML_3.98-1.16 nnet_7.3-12
[52] utf8_1.1.4 RJSONIO_1.3-1.1 labeling_0.3
[55] later_0.7.5 rlang_0.3.1 munsell_0.5.0
[58] tools_3.5.1 cli_1.1.0 evaluate_0.13
[61] yaml_2.2.0 processx_3.2.1 knitr_1.23
[64] mime_0.5 whisker_0.3-2 xml2_1.2.0
[67] compiler_3.5.1 rstudioapi_0.10 plotly_4.8.0
[70] e1071_1.7-0 spacetime_1.2-2 lhs_0.16
[73] stringi_1.2.4 ps_1.2.1 gdtools_0.1.7
[76] plot3D_1.1.1 Matrix_1.2-14 classInt_0.2-3
[79] pillar_1.3.1 plotKML_0.5-9 data.table_1.11.8
[82] httpuv_1.4.5 colorRamps_2.3 R6_2.2.2
[85] promises_1.0.1 gridExtra_2.3 codetools_0.2-15
[88] MASS_7.3-50 assertthat_0.2.0 rprojroot_1.3-2
[91] withr_2.1.2 hms_0.4.2 parallel_3.5.1
[94] grid_3.5.1 rpart_4.1-13 tidyr_0.8.2
[97] class_7.3-14 rmarkdown_1.12 misc3d_0.8-4
[100] mco_1.0-15.1 git2r_0.23.0 shiny_1.2.0
[103] base64enc_0.1-3