Result Detail – Random Forest

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Result Information
Result ID
144
Type
Random Forest
Status
completed
Dataset ID
698
Created
2026-09-21 02:14:14.068246
Saved Model
Available — Download .pkl
Model Parameters
Model Type
regression
Estimators
60
Max Depth
None
Pre-Modeling Statistics (Pearson, Spearman, ANOVA)
Feature Pearson r Pearson p Pearson FDR Spearman ρ Spearman FDR ANOVA F ANOVA FDR Significant?
PLOT -0.0656 0.2206 0.3219 -0.1974 0.0003 1.5057 0.3219 ✓ Yes
ENV_SOILOC 0.1181 0.0271 0.1291 0.3507 0.0000 4.9246 0.1291 ✓ Yes
ENV_SOILTC 0.1175 0.0280 0.1291 0.3547 0.0000 4.8708 0.1291 ✓ Yes
ENV_SOILTN 0.1145 0.0323 0.1291 0.3459 0.0000 4.6215 0.1291 ✓ Yes
ENV_PH 0.0045 0.9333 0.9333 0.0707 0.2078 0.0070 0.9333 ✗ No
ENV_SOILCLAY 0.0382 0.4761 0.5951 0.1408 0.0104 0.5089 0.5951 ✓ Yes
ENV_SOILSILT 0.0994 0.0632 0.2105 0.2985 0.0000 3.4745 0.2105 ✓ Yes
ENV_SOILSAND -0.0742 0.1662 0.3194 -0.2294 0.0000 1.9253 0.3194 ✓ Yes
ENV_SOILMOIS 0.1285 0.0162 0.1291 0.2213 0.0001 5.8410 0.1291 ✓ Yes
ENV_SOILP 0.0725 0.1757 0.3194 0.0586 0.2888 1.8412 0.3194 ✗ No
PC_SHOOTMASS 0.0867 0.1056 0.2639 0.2249 0.0000 2.6331 0.2639 ✓ Yes
PC_LITTERMASS 0.0166 0.7571 0.7970 0.1343 0.0140 0.0958 0.7970 ✓ Yes
COVER_TOTAL_JUL 0.0223 0.6775 0.7528 0.1862 0.0007 0.1732 0.7528 ✓ Yes
COVER_RICHNESS_JUL -0.0890 0.0965 0.2639 -0.0519 0.3331 2.7767 0.2639 ✗ No
PASTURE_TOTALAREA -0.0803 0.1339 0.2975 -0.2690 0.0000 2.2575 0.2975 ✓ Yes
LAT 0.0654 0.2223 0.3219 0.2422 0.0000 1.4951 0.3219 ✓ Yes
LONG 0.0650 0.2253 0.3219 0.2312 0.0000 1.4754 0.3219 ✓ Yes
ENV_SOILNO3 0.9494 0.0000 0.0000 0.4730 0.0000 3179.4291 0.0000 ✓ Yes
SOIL_EC_uS_cm -0.0392 0.4652 0.5951 -0.1437 0.0094 0.5345 0.5951 ✓ Yes
SOIL_CO2_mg_per_g 0.0296 0.5812 0.6838 0.1618 0.0034 0.3049 0.6838 ✓ Yes
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Feature Importance (Random Forest)
Rank Feature Importance (Mean) SD
1 ENV_SOILNO3 0.8195 0.0729
2 ENV_SOILTN 0.0214 0.0052
3 ENV_SOILMOIS 0.0169 0.0068
4 PC_LITTERMASS 0.0119 0.0088
5 ENV_SOILTC 0.0114 0.0056
6 ENV_SOILSILT 0.0112 0.0067
7 LAT 0.0111 0.0032
8 ENV_SOILOC 0.0111 0.0046
9 ENV_PH 0.0108 0.0050
10 LONG 0.0107 0.0052
11 ENV_SOILSAND 0.0102 0.0019
12 PC_SHOOTMASS 0.0085 0.0041
13 PLOT 0.0082 0.0038
14 PASTURE_TOTALAREA 0.0080 0.0038
15 ENV_SOILCLAY 0.0078 0.0041
16 COVER_TOTAL_JUL 0.0074 0.0040
17 ENV_SOILP 0.0062 0.0025
18 COVER_RICHNESS_JUL 0.0051 0.0017
19 SOIL_CO2_mg_per_g 0.0019 0.0014
20 SOIL_EC_uS_cm 0.0008 0.0007
Download CSV
Permutation Importance
Rank Feature Perm. Importance (Mean) SD
1 ENV_SOILNO3 0.9240 0.2616
2 ENV_SOILTN 0.1027 0.0707
3 PASTURE_TOTALAREA 0.0319 0.0502
4 ENV_SOILMOIS 0.0213 0.0189
5 ENV_SOILCLAY 0.0145 0.0097
6 PLOT 0.0113 0.0086
7 ENV_SOILSAND 0.0104 0.0075
8 ENV_SOILSILT 0.0064 0.0234
9 LAT 0.0053 0.0058
10 LONG 0.0051 0.0079
11 ENV_SOILTC 0.0026 0.0113
12 SOIL_CO2_mg_per_g 0.0017 0.0014
13 ENV_SOILOC 0.0012 0.0078
14 SOIL_EC_uS_cm 0.0009 0.0007
15 ENV_SOILP 0.0001 0.0010
16 PC_LITTERMASS -0.0019 0.0069
17 COVER_RICHNESS_JUL -0.0023 0.0028
18 PC_SHOOTMASS -0.0025 0.0048
19 ENV_PH -0.0028 0.0029
20 COVER_TOTAL_JUL -0.0064 0.0057
Download CSV
SHAP Feature Importance
Rank Feature SHAP Mean |Abs|
1 ENV_SOILNO3 2.6035
2 ENV_SOILTN 0.3270
3 PASTURE_TOTALAREA 0.2388
4 PC_LITTERMASS 0.2162
5 ENV_SOILMOIS 0.2057
6 LAT 0.1839
7 ENV_SOILOC 0.1837
8 ENV_SOILSILT 0.1672
9 ENV_SOILTC 0.1262
10 PLOT 0.1131
11 ENV_PH 0.1098
12 LONG 0.1073
13 COVER_TOTAL_JUL 0.0966
14 ENV_SOILSAND 0.0656
15 ENV_SOILCLAY 0.0613
16 PC_SHOOTMASS 0.0545
17 COVER_RICHNESS_JUL 0.0327
18 ENV_SOILP 0.0312
19 SOIL_EC_uS_cm 0.0276
20 SOIL_CO2_mg_per_g 0.0195
Download CSV
Model Metrics
Split R² MSE
Split 1 0.3084 5.9581
Split 2 0.4836 6.3112
Split 3 0.8243 32.6911
Mean 0.5388 14.9868
Download CSV
Figures
Feature importance Download PNG
Feature importance
Permutation importance Download PNG
Permutation importance
SHAP summary Download PNG
SHAP summary
Observed vs predicted (out-of-fold) Download PNG
Observed vs predicted (out-of-fold)
Cross-validation score distribution Download PNG
Cross-validation score distribution
Pathway Analysis (same dataset)
Run Pathway Analysis

Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #144)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#698 gallery — catg_example_03_soil_nitrogen_availability.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/413ee895cd9742ed95bd1502567c309a Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:14:14.068246

Parameters used

ParameterValue
task_typeregression
model_typeregression
target_columnENV_SOILNH4
n_estimators60
n_splits3
test_size0.25
run_univariateTrue
run_permutationTrue
run_shapTrue
permutation_repeats10
fdr_threshold0.05

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made. 

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=60, random_state=42)
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)