Result Detail – Random Forest

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Result Information

Result ID: 145
Type: Random Forest
Status: completed
Dataset ID: 699
Created: 2026-09-21 02:14:42.722532
Saved Model: Available — Download .pkl

Possible data leakage detected:

Identifier-like column(s) used as features: PLOT. These are bookkeeping fields; high importance for them usually reflects row ordering, not biology. Consider excluding them.

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	0.0327	0.5170	0.7545	0.0396	0.6266	0.4206	0.7545	✗ No
ENV_SOILNH4	-0.0655	0.1937	0.6241	-0.0088	0.9248	1.6951	0.6241	✗ No
ENV_SOILNO3	-0.0852	0.0903	0.3274	-0.0869	0.2614	2.8827	0.3274	✗ No
ENV_SOILP	-0.0423	0.4015	0.7502	-0.0244	0.7289	0.7054	0.7502	✗ No
ENV_SOILOC	-0.0274	0.5863	0.7729	-0.0811	0.2614	0.2966	0.7729	✗ No
ENV_SOILTN	-0.0180	0.7207	0.8373	-0.0366	0.6456	0.1280	0.8373	✗ No
ENV_SOILTC	-0.0132	0.7936	0.8524	-0.0398	0.6266	0.0685	0.8524	✗ No
ENV_PH	-0.0469	0.3521	0.7502	-0.0304	0.7208	0.8680	0.7502	✗ No
ENV_SOILCLAY	-0.0568	0.2596	0.6843	-0.0808	0.2614	1.2747	0.6843	✗ No
ENV_SOILSILT	-0.0193	0.7020	0.8373	-0.0580	0.4106	0.1467	0.8373	✗ No
ENV_SOILSAND	0.0382	0.4484	0.7545	0.0818	0.2614	0.5758	0.7545	✗ No
ENV_SOILMOIS	-0.0412	0.4139	0.7502	-0.0859	0.2614	0.6690	0.7502	✗ No
PC_SHOOTMASS	-0.0286	0.5701	0.7729	-0.1051	0.1763	0.3230	0.7729	✗ No
PC_WOODMASS	0.0575	0.2534	0.6843	0.0698	0.3201	1.3084	0.6843	✗ No
PC_LITTERMASS	-0.0924	0.0662	0.2742	-0.1464	0.0204	3.3942	0.2742	✓ Yes
PC_HEIGHT	-0.0324	0.5203	0.7545	-0.0052	0.9508	0.4140	0.7545	✗ No
COVER_RICHNESS_JUN	0.4070	0.0000	0.0000	0.4176	0.0000	78.2461	0.0000	✓ Yes
COVER_TOTAL_JUN	0.1136	0.0238	0.1382	0.0574	0.4106	5.1470	0.1382	✗ No
COVER_RICHNESS_AUG	0.3853	0.0000	0.0000	0.4381	0.0000	68.7058	0.0000	✓ Yes
COVER_TOTAL_AUG	-0.0165	0.7430	0.8373	-0.0756	0.2974	0.1076	0.8373	✗ No
PASTURE_TOTALAREA	0.0479	0.3417	0.7502	0.0714	0.3201	0.9063	0.7502	✗ No
PASTURE_FORESTAREA	-0.0036	0.9425	0.9425	-0.0256	0.7289	0.0052	0.9425	✗ No
ENV_AIRTEMP	-0.0038	0.9401	0.9425	-0.0134	0.8819	0.0056	0.9425	✗ No
GH_COUNT	-0.0333	0.5092	0.7545	-0.0245	0.7289	0.4366	0.7545	✗ No
COVER_RICHNESS_JUL	0.8524	0.0000	0.0000	0.8063	0.0000	1047.0461	0.0000	✓ Yes
COVER_TOTAL_JUL	0.6748	0.0000	0.0000	0.4609	0.0000	329.4078	0.0000	✓ Yes
ENV_PROBEMOIS_AUG	-0.0419	0.4058	0.7502	-0.0615	0.4028	0.6926	0.7502	✗ No
ENV_PROBEMOIS_JUL	-0.0160	0.7507	0.8373	-0.0010	0.9845	0.1011	0.8373	✗ No
ENV_PROBEMOIS_JUN	-0.1013	0.0440	0.2128	-0.0960	0.2336	4.0817	0.2128	✗ No

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Feature Importance (Random Forest)

Rank	Feature	Importance (Mean)	SD
1	COVER_RICHNESS_JUL	0.5566	0.0560
2	COVER_TOTAL_JUL	0.2464	0.0551
3	PC_LITTERMASS	0.0157	0.0025
4	ENV_PROBEMOIS_JUN	0.0132	0.0049
5	ENV_PH	0.0108	0.0021
6	ENV_AIRTEMP	0.0108	0.0026
7	COVER_RICHNESS_AUG	0.0105	0.0024
8	PC_SHOOTMASS	0.0097	0.0025
9	ENV_SOILP	0.0097	0.0014
10	ENV_SOILCLAY	0.0092	0.0008
11	COVER_TOTAL_AUG	0.0083	0.0017
12	COVER_RICHNESS_JUN	0.0081	0.0014
13	ENV_SOILNO3	0.0079	0.0007
14	PLOT	0.0077	0.0011
15	COVER_TOTAL_JUN	0.0076	0.0011
16	ENV_SOILMOIS	0.0064	0.0006
17	ENV_PROBEMOIS_AUG	0.0061	0.0015
18	PC_HEIGHT	0.0061	0.0008
19	ENV_SOILSAND	0.0057	0.0005
20	ENV_SOILSILT	0.0055	0.0001
21	PC_WOODMASS	0.0053	0.0021
22	ENV_SOILOC	0.0051	0.0005
23	ENV_PROBEMOIS_JUL	0.0048	0.0005
24	ENV_SOILNH4	0.0045	0.0006
25	GH_COUNT	0.0040	0.0007
26	ENV_SOILTN	0.0039	0.0014
27	PASTURE_FORESTAREA	0.0038	0.0014
28	ENV_SOILTC	0.0037	0.0006
29	PASTURE_TOTALAREA	0.0029	0.0006

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Permutation Importance

Rank	Feature	Perm. Importance (Mean)	SD
1	COVER_RICHNESS_JUL	0.7960	0.0963
2	COVER_TOTAL_JUL	0.1961	0.0215
3	PC_LITTERMASS	0.0112	0.0040
4	ENV_PROBEMOIS_JUN	0.0059	0.0026
5	COVER_TOTAL_AUG	0.0031	0.0012
6	ENV_AIRTEMP	0.0031	0.0012
7	ENV_SOILMOIS	0.0031	0.0031
8	PC_WOODMASS	0.0026	0.0023
9	ENV_SOILOC	0.0020	0.0008
10	ENV_SOILSAND	0.0019	0.0032
11	PLOT	0.0017	0.0019
12	COVER_RICHNESS_JUN	0.0017	0.0018
13	PASTURE_FORESTAREA	0.0014	0.0007
14	COVER_TOTAL_JUN	0.0012	0.0015
15	ENV_SOILNH4	0.0010	0.0015
16	ENV_PH	0.0010	0.0010
17	ENV_PROBEMOIS_JUL	0.0008	0.0007
18	PC_SHOOTMASS	0.0005	0.0008
19	PASTURE_TOTALAREA	0.0004	0.0003
20	GH_COUNT	0.0003	0.0017
21	ENV_SOILSILT	0.0000	0.0013
22	COVER_RICHNESS_AUG	-0.0000	0.0021
23	PC_HEIGHT	-0.0001	0.0009
24	ENV_SOILTC	-0.0003	0.0011
25	ENV_SOILTN	-0.0003	0.0008
26	ENV_PROBEMOIS_AUG	-0.0007	0.0027
27	ENV_SOILNO3	-0.0008	0.0014
28	ENV_SOILP	-0.0014	0.0018
29	ENV_SOILCLAY	-0.0015	0.0037

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SHAP Feature Importance

Rank	Feature	SHAP Mean \|Abs\|
1	COVER_RICHNESS_JUL	0.2029
2	COVER_TOTAL_JUL	0.0809
3	PC_LITTERMASS	0.0143
4	ENV_PROBEMOIS_JUN	0.0143
5	ENV_PH	0.0060
6	ENV_AIRTEMP	0.0049
7	COVER_TOTAL_AUG	0.0046
8	COVER_TOTAL_JUN	0.0045
9	COVER_RICHNESS_AUG	0.0040
10	PC_HEIGHT	0.0038
11	PC_SHOOTMASS	0.0031
12	ENV_SOILCLAY	0.0030
13	ENV_SOILOC	0.0028
14	ENV_SOILP	0.0027
15	ENV_PROBEMOIS_AUG	0.0027
16	ENV_SOILNO3	0.0026
17	ENV_SOILSILT	0.0026
18	PLOT	0.0025
19	COVER_RICHNESS_JUN	0.0022
20	GH_COUNT	0.0021
21	ENV_PROBEMOIS_JUL	0.0021
22	ENV_SOILNH4	0.0019
23	ENV_SOILSAND	0.0018
24	ENV_SOILTC	0.0018
25	ENV_SOILMOIS	0.0017
26	PASTURE_TOTALAREA	0.0012
27	ENV_SOILTN	0.0012
28	PC_WOODMASS	0.0010
29	PASTURE_FORESTAREA	0.0008

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Model Metrics

Split	R²	MSE
Split 1	0.7461	0.0302
Split 2	0.8046	0.0268
Split 3	0.8026	0.0286
Mean	0.7844	0.0286

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Figures

Feature importance Download PNG

Permutation importance Download PNG

SHAP summary Download PNG

Observed vs predicted (out-of-fold) Download PNG

Cross-validation score distribution Download PNG

Pathway Analysis (same dataset)

Run Pathway Analysis

Method & Provenance

Method: Random Forest (Regression)
Method key: random_forest (result type: random_forest, result #145)
Engine: Python · scikit-learn v1.8.0
Dataset(s): #699 gallery — catg_example_04_vegetation_diversity.csv
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/023c0e89aaf94a488763f77c20de38a3 Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:14:42.722532

Parameters used

Parameter	Value
`task_type`	regression
`model_type`	regression
`target_column`	COVER_SHANNON_JUL
`n_estimators`	60
`n_splits`	3
`test_size`	0.25
`run_univariate`	True
`run_permutation`	True
`run_shap`	True
`permutation_repeats`	10
`fdr_threshold`	0.05

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)