Stacked generalization of statistical learners – a case study with soil iron content in Brazil
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Transcript of Stacked generalization of statistical learners – a case study with soil iron content in Brazil
Stacked generalization of statisticallearners – a case study with soil iron content in Brazil
Pedometrics 2017, Wageningen, NLThursday 29 JuneParallel session on Machine learning for soil mapping (5H)Chaired by Laura Poggio
A. (Alessandro) Samuel-Rosa* & R. S. D. Dalmolin
25 years ago
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
25 years ago
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
NowadaysModel-based Gaussian and robust geostatistics (georob)
Andreas Papritz (May 9, 2017)
Nowadays
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Legacy soil dataSuboptimal geographic/feature coverageExtrapolation/reference area method
25 years ago
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Stacked generalization (regression)
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Goals1) Combine statistical learners, and2) Improve generalization accuracy
Learning error Generalization error
?
Stacked generalization (regression)
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Strategy1) Metamodel with cross-validation predictions as
covariates
2) Constrained metamodel coefficients to drop redundant covariates/models
Case study
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
● Brazilian soil database
● 5770 soil profiles (22,981 records)
● 70% learning / 30% evaluation
● iron ~ depth + taxon + colour + parent + carbon + clay + ph
1. Linear regression with stepwise selection
2. Multivariate adaptive regression splines
3. Regression random forest4. Single-hidden-layer neural
network5. Weighted k-nearest
neighbor regression6. Support vector machine
with polynomial kernel10-fold cross-validation
Metamodel
(Meta)Model evaluation
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Learner ME MSE MAE RMSE AVE
rf 0.56 2369.58 27.82 48.68 0.55 0.5378
svm -5.24 2600.78 28.69 51.00 0.51 0.2773
kknn -0.19 2427.48 28.75 49.27 0.54 0.0796
mars -0.19 2577.03 29.84 50.76 0.51 0.0752
nnet 0.17 2721.52 31.76 52.17 0.49 0.0403
lm -0.31 2875.50 32.70 53.62 0.46 0.0000
Metamodel -0.53 2349.93 27.63 48.48 0.56 1.0101
Evaluation
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Evaluation
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
Conclusions
Machine learning for soil mapping (5H)Stacked generalization of statistical learners – a case study with soil iron in Brazil
● Stack of learners is superior– Always?
● Easy to compute prediction error variance– Standard regression/classification formulas
● Environmental interpretation– Danger zone!?
● Cannot make miracles– Data quality/quantity, diversity of learners
Stacked generalization of statisticallearners – a case study with soil iron content in Brazil
A. (Alessandro) Samuel-Rosa* & R. S. D. Dalmolin
This project was developed under the auspices of the Postgraduate Program in Soil Science of the Federal University of Santa Maria as part of the National Postdoctoral Program (PNPD) of the Coordination for Advancement for High Level Personnel (CAPES) of the Ministry of Education of Brazil.