Result Detail – Random Forest Help

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Result Information

Result ID: 142
Type: Random Forest
Status: completed
Dataset ID: 694
Created: 2026-09-21 02:13:38.003816
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) Help

Feature	Pearson r	Pearson p	Pearson FDR	Spearman ρ	Spearman FDR	ANOVA F	ANOVA FDR	Significant?
PLOT	-0.3602	0.0000	0.0000	-0.2762	0.0000	58.3020	0.0000	✓ Yes
ENV_SOILNH4	0.0825	0.1025	0.1209	0.1837	0.0003	2.6781	0.1209	✓ Yes
ENV_SOILNO3	0.0367	0.4677	0.5144	0.0909	0.0791	0.5285	0.5144	✗ No
ENV_SOILP	0.2028	0.0001	0.0001	0.1248	0.0157	16.7772	0.0001	✓ Yes
ENV_SOILOC	0.4835	0.0000	0.0000	0.4601	0.0000	119.2943	0.0000	✓ Yes
ENV_SOILTN	0.4808	0.0000	0.0000	0.4570	0.0000	117.5512	0.0000	✓ Yes
ENV_SOILTC	0.4834	0.0000	0.0000	0.4595	0.0000	119.2008	0.0000	✓ Yes
ENV_PH	-0.0157	0.7558	0.7558	-0.0387	0.4727	0.0968	0.7558	✗ No
ENV_SOILCLAY	0.3548	0.0000	0.0000	0.3541	0.0000	56.2918	0.0000	✓ Yes
ENV_SOILSILT	0.4313	0.0000	0.0000	0.4004	0.0000	89.3405	0.0000	✓ Yes
ENV_SOILSAND	-0.4132	0.0000	0.0000	-0.3831	0.0000	80.5038	0.0000	✓ Yes
ENV_SOILMOIS	0.4618	0.0000	0.0000	0.4453	0.0000	105.9672	0.0000	✓ Yes
COVER_RICHNESS_JUN	-0.1236	0.0142	0.0180	-0.2224	0.0000	6.0689	0.0180	✓ Yes
COVER_TOTAL_JUN	0.3246	0.0000	0.0000	0.3628	0.0000	46.0578	0.0000	✓ Yes
COVER_SHANNON_JUN	-0.1289	0.0105	0.0139	-0.2211	0.0000	6.6112	0.0139	✓ Yes
COVER_RICHNESS_JUL	-0.0952	0.0593	0.0724	-0.1762	0.0005	3.5783	0.0724	✓ Yes
COVER_TOTAL_JUL	0.3040	0.0000	0.0000	0.4258	0.0000	39.8262	0.0000	✓ Yes
COVER_SHANNON_JUL	-0.0290	0.5667	0.6032	-0.1081	0.0366	0.3289	0.6032	✓ Yes
COVER_RICHNESS_AUG	-0.1788	0.0004	0.0005	-0.2615	0.0000	12.9125	0.0005	✓ Yes
COVER_TOTAL_AUG	0.3803	0.0000	0.0000	0.4278	0.0000	66.1066	0.0000	✓ Yes
COVER_SHANNON_AUG	-0.1623	0.0012	0.0017	-0.2305	0.0000	10.5809	0.0017	✓ Yes
LAT	0.3925	0.0000	0.0000	0.2620	0.0000	71.1924	0.0000	✓ Yes
LONG	0.3863	0.0000	0.0000	0.2380	0.0000	68.5667	0.0000	✓ Yes
PASTURE_TOTALAREA	-0.3975	0.0000	0.0000	-0.3756	0.0000	73.3916	0.0000	✓ Yes
SUBPASTURE_TOTALAREA	-0.3140	0.0000	0.0000	-0.3013	0.0000	42.7582	0.0000	✓ Yes
PASTURE_FORESTAREA	0.2970	0.0000	0.0000	0.2509	0.0000	37.8304	0.0000	✓ Yes
PASTURE_WETLANDAREA	-0.0718	0.1552	0.1766	0.0063	0.9004	2.0286	0.1766	✗ No
ENV_AIRTEMP	0.0248	0.6245	0.6440	-0.0134	0.8151	0.2400	0.6440	✗ No
GH_COUNT	-0.3009	0.0000	0.0000	-0.2971	0.0000	38.9156	0.0000	✓ Yes
PC_HEIGHT	0.4491	0.0000	0.0000	0.4438	0.0000	98.8059	0.0000	✓ Yes
ENV_PROBEMOIS_AUG	0.5002	0.0000	0.0000	0.4429	0.0000	130.4838	0.0000	✓ Yes
ENV_PROBEMOIS_JUL	0.3386	0.0000	0.0000	0.2924	0.0000	50.6438	0.0000	✓ Yes
ENV_PROBEMOIS_JUN	0.2328	0.0000	0.0000	0.2335	0.0000	22.4076	0.0000	✓ Yes

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Feature Importance (Random Forest) Help

Rank	Feature	Importance (Mean)	SD
1	ENV_PROBEMOIS_AUG	0.1058	0.0317
2	COVER_TOTAL_JUL	0.0678	0.0064
3	ENV_SOILOC	0.0625	0.0289
4	ENV_SOILTN	0.0582	0.0200
5	COVER_TOTAL_AUG	0.0519	0.0164
6	ENV_SOILTC	0.0494	0.0148
7	ENV_SOILMOIS	0.0440	0.0050
8	ENV_SOILP	0.0440	0.0185
9	COVER_TOTAL_JUN	0.0430	0.0129
10	PC_HEIGHT	0.0374	0.0068
11	PASTURE_FORESTAREA	0.0372	0.0168
12	PLOT	0.0354	0.0142
13	SUBPASTURE_TOTALAREA	0.0300	0.0049
14	LONG	0.0264	0.0027
15	ENV_SOILSILT	0.0262	0.0057
16	ENV_PROBEMOIS_JUN	0.0215	0.0069
17	ENV_PROBEMOIS_JUL	0.0215	0.0058
18	COVER_SHANNON_JUN	0.0215	0.0030
19	COVER_SHANNON_AUG	0.0213	0.0043
20	COVER_RICHNESS_AUG	0.0211	0.0053
21	PASTURE_TOTALAREA	0.0191	0.0063
22	ENV_PH	0.0190	0.0012
23	ENV_SOILNO3	0.0174	0.0023
24	ENV_SOILSAND	0.0156	0.0095
25	LAT	0.0154	0.0022
26	COVER_SHANNON_JUL	0.0142	0.0009
27	COVER_RICHNESS_JUN	0.0142	0.0065
28	ENV_SOILNH4	0.0138	0.0049
29	COVER_RICHNESS_JUL	0.0130	0.0029
30	ENV_SOILCLAY	0.0098	0.0037
31	PASTURE_WETLANDAREA	0.0079	0.0011
32	ENV_AIRTEMP	0.0075	0.0023
33	GH_COUNT	0.0074	0.0008

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Permutation Importance Help

Rank	Feature	Perm. Importance (Mean)	SD
1	SUBPASTURE_TOTALAREA	0.0838	0.0756
2	COVER_TOTAL_JUL	0.0371	0.0111
3	PC_HEIGHT	0.0330	0.0050
4	PLOT	0.0211	0.0139
5	COVER_TOTAL_AUG	0.0172	0.0119
6	ENV_PROBEMOIS_AUG	0.0169	0.0441
7	ENV_SOILP	0.0165	0.0035
8	LAT	0.0143	0.0060
9	ENV_SOILTC	0.0143	0.0472
10	LONG	0.0112	0.0091
11	ENV_PROBEMOIS_JUN	0.0106	0.0036
12	ENV_SOILOC	0.0091	0.0160
13	ENV_SOILTN	0.0091	0.0128
14	COVER_RICHNESS_AUG	0.0083	0.0063
15	PASTURE_WETLANDAREA	0.0079	0.0022
16	PASTURE_FORESTAREA	0.0068	0.0217
17	COVER_RICHNESS_JUN	0.0060	0.0045
18	PASTURE_TOTALAREA	0.0048	0.0043
19	COVER_SHANNON_JUN	0.0046	0.0029
20	COVER_RICHNESS_JUL	0.0044	0.0013
21	ENV_PROBEMOIS_JUL	0.0036	0.0040
22	ENV_SOILNO3	0.0034	0.0033
23	ENV_SOILCLAY	0.0033	0.0046
24	COVER_TOTAL_JUN	0.0030	0.0204
25	ENV_SOILNH4	0.0018	0.0013
26	ENV_PH	0.0012	0.0029
27	ENV_AIRTEMP	0.0008	0.0029
28	ENV_SOILSAND	0.0006	0.0048
29	GH_COUNT	-0.0007	0.0018
30	COVER_SHANNON_AUG	-0.0012	0.0049
31	COVER_SHANNON_JUL	-0.0016	0.0027
32	ENV_SOILSILT	-0.0058	0.0073
33	ENV_SOILMOIS	-0.0089	0.0305

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SHAP Feature Importance Help

Rank	Feature	SHAP Mean \|Abs\|
1	ENV_PROBEMOIS_AUG	1.6375
2	COVER_TOTAL_JUL	1.2206
3	ENV_SOILTN	0.7822
4	ENV_SOILTC	0.7653
5	COVER_TOTAL_AUG	0.6028
6	PASTURE_TOTALAREA	0.5172
7	PASTURE_FORESTAREA	0.4910
8	ENV_SOILMOIS	0.4853
9	PC_HEIGHT	0.4597
10	SUBPASTURE_TOTALAREA	0.4173
11	COVER_RICHNESS_AUG	0.4052
12	COVER_TOTAL_JUN	0.3391
13	ENV_SOILOC	0.2923
14	LONG	0.2694
15	COVER_SHANNON_AUG	0.2516
16	PLOT	0.2476
17	COVER_SHANNON_JUN	0.2435
18	COVER_SHANNON_JUL	0.2195
19	ENV_SOILP	0.2172
20	ENV_SOILSILT	0.1889
21	ENV_PH	0.1561
22	COVER_RICHNESS_JUL	0.1491
23	LAT	0.1297
24	ENV_PROBEMOIS_JUL	0.1252
25	COVER_RICHNESS_JUN	0.1155
26	ENV_PROBEMOIS_JUN	0.1060
27	ENV_SOILNH4	0.1033
28	PASTURE_WETLANDAREA	0.1005
29	ENV_SOILNO3	0.0946
30	ENV_SOILSAND	0.0713
31	GH_COUNT	0.0658
32	ENV_SOILCLAY	0.0653
33	ENV_AIRTEMP	0.0600

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Model Metrics

Split	R²	MSE
Split 1	0.4729	62.4129
Split 2	0.3846	44.3901
Split 3	0.1794	49.8954
Mean	0.3456	52.2328

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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) Help

Run Pathway Analysis

Method & Provenance

Method: Random Forest (Regression)
Method key: random_forest (result type: random_forest, result #142)
Engine: Python · scikit-learn v1.8.0
Dataset(s): #694 gallery — catg_example_01_forage_biomass.csv
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/96b7753d8e314763a196bb620423f685 Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:13:38.003816

Parameters used

Parameter	Value
`task_type`	regression
`model_type`	regression
`target_column`	PC_SHOOTMASS
`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)