Evaluating classifier performance on Diplodia sapinea
Patrick Schratz, Friedrich-Schiller-University Jena
Last updated: 2019-05-24
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Knit directory: 2018-model-comparison/
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1 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)
2 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.
3 Results structure
The structure of the following results presentation is as follows:
- Table view of all performances for each resampling setting
- Boxplot comparison for each pathogen and algorithm
- Aggregated performances for each pathogen and algorithm
4 Performance results
4.1 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 1820.59904.1.1 Boxplot comparison
4.1.2 Aggregated performances
4.2 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 445.12064.2.1 Boxplot comparison
4.2.2 Aggregated performances
4.3 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 440.40304.3.1 Boxplot comparison
4.3.2 Aggregated performances
4.4 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 0.6675564.4.1 Boxplot comparison
4.4.2 Aggregated performances
4.5 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 0.6519484.5.1 Boxplot comparison
4.5.2 Aggregated performances
5 Boxplot comparison of all algorithm/tuning settings
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6 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/LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-7.3.0/openblas-0.3.5-zncvk4jccaqyfl4z3vszaboeps6hyzta/lib/libopenblas_zen-r0.3.5.so
locale:
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[9] LC_ADDRESS=C LC_TELEPHONE=C
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attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
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[28] plyr_1.8.4 shapefiles_0.7 foreign_0.8-71
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[52] BBmisc_1.11 magrittr_1.5 mlr_2.13.9000
[55] ParamHelpers_1.11 drake_7.2.0
loaded via a namespace (and not attached):
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