Result Detail – Ml Result

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Result Information
Result ID
101
Type
Ml Result
Status
completed
Dataset ID
690
Created
2026-09-21 02:12:52.641122
ML Pipeline — Random Forest Regression Help
Model
Random Forest
Target Column
PC_SHOOTMASS
CV Splits
3
Split R²MSE
Split 1 0.2949 65.1131
Split 2 0.2642 69.0418
Split 3 0.2566 55.7114
Mean 0.2719 63.2888
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RankFeatureImportance
1 RC_SHOOTMASS 0.405673
2 ENV_SOILOC 0.177931
3 ENV_SOILMOIS 0.154279
4 ENV_SOILTN 0.133153
5 ENV_SOILTC 0.128963

Permutation importance was not included in this run.

SHAP analysis was not included in this run.

Feature Pearson rPearson pPearson FDR Spearman ρSpearman FDR ANOVA FANOVA FDR Significant?
PLOT -0.3566 0.0000 0.0000 -0.2676 0.0000 57.9998 0.0000 ✓ Yes
LAT 0.3911 0.0000 0.0000 0.2634 0.0000 71.8502 0.0000 ✓ Yes
LONG 0.3848 0.0000 0.0000 0.2388 0.0000 69.1789 0.0000 ✓ Yes
ENV_SOILNH4 0.0824 0.0996 0.1208 0.1870 0.0002 2.7239 0.1208 ✓ Yes
ENV_SOILNO3 0.0367 0.4639 0.5015 0.0933 0.0691 0.5376 0.5015 ✗ No
ENV_SOILP 0.2028 0.0000 0.0001 0.1271 0.0133 17.0726 0.0001 ✓ Yes
ENV_SOILOC 0.4830 0.0000 0.0000 0.4605 0.0000 121.1279 0.0000 ✓ Yes
ENV_SOILTN 0.4801 0.0000 0.0000 0.4567 0.0000 119.2385 0.0000 ✓ Yes
ENV_SOILTC 0.4829 0.0000 0.0000 0.4597 0.0000 121.0029 0.0000 ✓ Yes
ENV_PH -0.0157 0.7540 0.7540 -0.0332 0.5344 0.0983 0.7540 ✗ No
ENV_SOILCLAY 0.3541 0.0000 0.0000 0.3538 0.0000 57.0470 0.0000 ✓ Yes
ENV_SOILSILT 0.4306 0.0000 0.0000 0.4009 0.0000 90.5771 0.0000 ✓ Yes
ENV_SOILSAND -0.4124 0.0000 0.0000 -0.3838 0.0000 81.5668 0.0000 ✓ Yes
ENV_SOILMOIS 0.4614 0.0000 0.0000 0.4451 0.0000 107.6683 0.0000 ✓ Yes
RC_SHOOTMASS 0.5332 0.0000 0.0000 0.6073 0.0000 158.0640 0.0000 ✓ Yes
RC_WOODMASS 0.1152 0.0212 0.0283 0.1585 0.0019 5.3525 0.0283 ✓ Yes
RC_LITTERMASS 0.4564 0.0000 0.0000 0.3922 0.0000 104.7397 0.0000 ✓ Yes
RC_LITTERWMASS 0.0591 0.2383 0.2724 0.1293 0.0120 1.3947 0.2724 ✓ Yes
PC_WOODMASS 0.2348 0.0000 0.0000 0.2313 0.0000 23.2218 0.0000 ✓ Yes
PC_LITTERMASS 0.4327 0.0000 0.0000 0.4261 0.0000 91.6693 0.0000 ✓ Yes
PC_LITTERWMASS 0.1439 0.0039 0.0058 0.1252 0.0144 8.4115 0.0058 ✓ Yes
PC_ROOTMASS 0.1007 0.0442 0.0571 0.2809 0.0000 4.0741 0.0571 ✓ Yes
PC_HEIGHT 0.4465 0.0000 0.0000 0.4329 0.0000 99.0837 0.0000 ✓ Yes
GH_COUNT -0.2974 0.0000 0.0000 -0.2940 0.0000 38.6160 0.0000 ✓ Yes
ENV_AIRTEMP 0.0247 0.6220 0.6379 -0.0142 0.7971 0.2435 0.6379 ✗ No
COVER_RICHNESS_JUN -0.1236 0.0134 0.0185 -0.2189 0.0000 6.1702 0.0185 ✓ Yes
COVER_TOTAL_JUN 0.3239 0.0000 0.0000 0.3605 0.0000 46.6550 0.0000 ✓ Yes
COVER_SHANNON_JUN -0.1286 0.0100 0.0143 -0.2196 0.0000 6.6913 0.0143 ✓ Yes
COVER_RICHNESS_JUL -0.0938 0.0609 0.0761 -0.1713 0.0008 3.5336 0.0761 ✓ Yes
COVER_TOTAL_JUL 0.3024 0.0000 0.0000 0.4263 0.0000 40.0461 0.0000 ✓ Yes
COVER_SHANNON_JUL -0.0286 0.5682 0.5981 -0.1044 0.0421 0.3263 0.5981 ✓ Yes
COVER_RICHNESS_AUG -0.1785 0.0003 0.0005 -0.2616 0.0000 13.0987 0.0005 ✓ Yes
COVER_TOTAL_AUG 0.3798 0.0000 0.0000 0.4245 0.0000 67.0752 0.0000 ✓ Yes
COVER_SHANNON_AUG -0.1615 0.0012 0.0018 -0.2277 0.0000 10.6585 0.0018 ✓ Yes
PASTURE_TOTALAREA -0.3966 0.0000 0.0000 -0.3771 0.0000 74.3077 0.0000 ✓ Yes
SUBPASTURE_TOTALAREA -0.3128 0.0000 0.0000 -0.3020 0.0000 43.1698 0.0000 ✓ Yes
PASTURE_FORESTAREA 0.2953 0.0000 0.0000 0.2456 0.0000 38.0232 0.0000 ✓ Yes
PASTURE_WETLANDAREA -0.0716 0.1531 0.1802 0.0093 0.8533 2.0486 0.1802 ✗ No
SUBPASTURE_FORESTAREA 0.2507 0.0000 0.0000 0.2206 0.0000 26.6878 0.0000 ✓ Yes
SUBPASTURE_WETLANDAREA 0.0440 0.3797 0.4219 0.0704 0.1730 0.7734 0.4219 ✗ No
Figures
Feature importance Download PNG
Feature importance
Cross-validation score distribution Download PNG
Cross-validation score distribution
Pathway Analysis (same dataset) Help
Run Pathway Analysis

Method & Provenance

Method
Random Forest
Method key
ml_result (result type: ml_result, result #101)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#690 gallery — cat_g_field_data_plots.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/a316fb7a42834890a899475de981b6f9 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:12:52.641122

Parameters used

ParameterValue
model_type random_forest
target_column PC_SHOOTMASS
n_splits 3

Execution trace

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

# Models compared via app.services.ml_model_registry
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)