Landsat FIA AGB 0.0.5: High Class

Mike Mahoney true
2021-07-20

Evaluation Results

Last version of FIA models: 2021-07-06 Take Two

Comparison Hudak method: 2021-07-19 0.0.5 High Class

Change Summary

RF (ranger) GBM (LightGBM) SVM (kernlab) Ensemble (model weighted) Ensemble (RMSE weighted)
RMSE 46.998 47.494 48.081 46.714 46.540
MBE 1.058 0.154 -0.843 0.770 0.134
R2 0.666 0.658 0.650 0.669 0.673

AGB Distribution

summary(bind_rows(training, testing)$agb_mgha)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    0.00   61.55   79.01  139.60  425.00 

Validation Results

RMSE Min Median Max
Rf 45.144 48.646 51.930
Lgb 45.090 48.580 52.525
Svm 45.039 50.191 88.470
Ensemble 44.548 48.860 57.898
R2 Min Median Max
rf 0.603 0.644 0.682
lgb 0.595 0.641 0.681
svm 0.173 0.628 0.670
ensemble 0.604 0.652 0.690

Metadata

Ensembles

      lgb        rf       svm 
0.3326046 0.3396057 0.3277897 

Call:
lm(formula = agb_mgha ~ rf_pred * lgb_pred * svm_pred, data = pred_values)

Residuals:
     Min       1Q   Median       3Q      Max 
-189.558  -21.504   -4.116   16.588  277.471 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.259e-01  4.117e-01   0.306  0.75969    
rf_pred                    2.317e-01  3.186e-02   7.270 3.61e-13 ***
lgb_pred                   3.614e-01  3.370e-02  10.724  < 2e-16 ***
svm_pred                   2.991e-02  1.096e-02   2.729  0.00636 ** 
rf_pred:lgb_pred           2.253e-03  1.317e-04  17.112  < 2e-16 ***
rf_pred:svm_pred           3.978e-03  3.343e-04  11.898  < 2e-16 ***
lgb_pred:svm_pred         -3.098e-04  3.402e-04  -0.911  0.36251    
rf_pred:lgb_pred:svm_pred -2.107e-05  1.246e-06 -16.911  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 48.18 on 91992 degrees of freedom
Multiple R-squared:  0.6503,    Adjusted R-squared:  0.6502 
F-statistic: 2.443e+04 on 7 and 91992 DF,  p-value: < 2.2e-16

Coverages

\(n\) and \(p\)

Component Models

Random forest:

$num.trees
[1] 1000

$mtry
[1] 10

$min.node.size
[1] 2

$sample.fraction
[1] 1

$splitrule
[1] "variance"

$replace
[1] TRUE

$formula
agb_mgha ~ .

LGB:

$learning_rate
[1] 0.1

$nrounds
[1] 50

$num_leaves
[1] 14

$max_depth
[1] -1

$extra_trees
[1] TRUE

$min_data_in_leaf
[1] 10

$bagging_fraction
[1] 0.9

$bagging_freq
[1] 10

$feature_fraction
[1] 0.9

$min_data_in_bin
[1] 13

$lambda_l1
[1] 2

$lambda_l2
[1] 2

$force_col_wise
[1] TRUE

SVM:

$x
agb_mgha ~ .

$kernel
[1] "laplacedot"

$type
[1] "eps-svr"

$kpar
$kpar$sigma
[1] 0.03125


$C
[1] 16

$epsilon
[1] 0.0625

$nu
[1] 0.2

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Citation

For attribution, please cite this work as

Mahoney (2021, July 20). CAFRI Labs: Landsat FIA AGB 0.0.5: High Class. Retrieved from https://cafri-labs.github.io/acceptable-growing-stock/posts/landsat-fia-agb-005-high-class/

BibTeX citation

@misc{mahoney2021landsat,
  author = {Mahoney, Mike},
  title = {CAFRI Labs: Landsat FIA AGB 0.0.5: High Class},
  url = {https://cafri-labs.github.io/acceptable-growing-stock/posts/landsat-fia-agb-005-high-class/},
  year = {2021}
}