Result Detail – Random Forest Help

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Result Information

Result ID: 141
Type: Random Forest
Status: completed
Dataset ID: 695
Created: 2026-09-21 02:13:18.142034
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	0.0948	0.0604	0.0712	0.2332	0.0000	3.5459	0.0712	✓ Yes
ENV_SOILNO3	0.0490	0.3325	0.3658	0.1557	0.0023	0.9414	0.3658	✓ Yes
ENV_SOILP	0.2086	0.0000	0.0000	0.1279	0.0127	17.7800	0.0000	✓ Yes
ENV_SOILOC	0.4799	0.0000	0.0000	0.4664	0.0000	117.0170	0.0000	✓ Yes
ENV_SOILTN	0.4810	0.0000	0.0000	0.4640	0.0000	117.6691	0.0000	✓ Yes
ENV_SOILTC	0.4838	0.0000	0.0000	0.4658	0.0000	119.4975	0.0000	✓ Yes
ENV_PH	-0.0175	0.7289	0.7289	-0.0393	0.4651	0.1203	0.7289	✗ No
ENV_SOILCLAY	0.3526	0.0000	0.0000	0.3511	0.0000	55.5275	0.0000	✓ Yes
ENV_SOILSILT	0.4335	0.0000	0.0000	0.4026	0.0000	90.4940	0.0000	✓ Yes
ENV_SOILSAND	-0.4135	0.0000	0.0000	-0.3816	0.0000	80.6270	0.0000	✓ Yes
ENV_SOILMOIS	0.4648	0.0000	0.0000	0.4496	0.0000	107.7234	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	0.0712	-0.1762	0.0005	3.5783	0.0712	✓ 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	0.5844	-0.1081	0.0354	0.3289	0.5844	✓ 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	0.0357	0.4805	0.5115	-0.0067	0.9004	0.4987	0.5115	✗ No
GH_COUNT	-0.3535	0.0000	0.0000	-0.3948	0.0000	55.8253	0.0000	✓ Yes
PC_HEIGHT	0.5451	0.0000	0.0000	0.5156	0.0000	165.2833	0.0000	✓ Yes
ENV_PROBEMOIS_AUG	0.5002	0.0000	0.0000	0.4429	0.0000	130.4838	0.0000	✓ Yes
ENV_PROBEMOIS_JUL	0.3403	0.0000	0.0000	0.2940	0.0000	51.1940	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	PC_HEIGHT	0.2874	0.0534
2	ENV_PROBEMOIS_AUG	0.0650	0.0185
3	COVER_TOTAL_JUL	0.0506	0.0026
4	ENV_SOILMOIS	0.0329	0.0056
5	PLOT	0.0321	0.0089
6	ENV_SOILP	0.0306	0.0111
7	COVER_TOTAL_JUN	0.0293	0.0095
8	SUBPASTURE_TOTALAREA	0.0280	0.0034
9	COVER_TOTAL_AUG	0.0266	0.0022
10	ENV_SOILSILT	0.0246	0.0091
11	ENV_SOILSAND	0.0231	0.0054
12	ENV_SOILTC	0.0226	0.0070
13	LONG	0.0225	0.0035
14	ENV_SOILTN	0.0221	0.0091
15	GH_COUNT	0.0219	0.0055
16	COVER_RICHNESS_AUG	0.0207	0.0029
17	ENV_PROBEMOIS_JUN	0.0202	0.0024
18	COVER_SHANNON_JUN	0.0201	0.0039
19	ENV_PROBEMOIS_JUL	0.0192	0.0082
20	ENV_SOILOC	0.0183	0.0077
21	PASTURE_FORESTAREA	0.0181	0.0044
22	ENV_PH	0.0173	0.0011
23	COVER_SHANNON_AUG	0.0163	0.0025
24	ENV_AIRTEMP	0.0163	0.0034
25	ENV_SOILNO3	0.0161	0.0033
26	COVER_RICHNESS_JUN	0.0157	0.0035
27	COVER_SHANNON_JUL	0.0149	0.0023
28	ENV_SOILNH4	0.0142	0.0022
29	PASTURE_TOTALAREA	0.0140	0.0024
30	LAT	0.0138	0.0051
31	COVER_RICHNESS_JUL	0.0108	0.0030
32	ENV_SOILCLAY	0.0099	0.0013
33	PASTURE_WETLANDAREA	0.0050	0.0003

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Permutation Importance Help

Rank	Feature	Perm. Importance (Mean)	SD
1	PC_HEIGHT	0.1815	0.0811
2	SUBPASTURE_TOTALAREA	0.0530	0.0267
3	ENV_PROBEMOIS_JUN	0.0157	0.0110
4	COVER_RICHNESS_JUL	0.0138	0.0104
5	LONG	0.0105	0.0100
6	COVER_TOTAL_JUL	0.0096	0.0132
7	PLOT	0.0091	0.0041
8	PASTURE_TOTALAREA	0.0081	0.0117
9	COVER_TOTAL_JUN	0.0072	0.0114
10	COVER_SHANNON_JUN	0.0060	0.0048
11	COVER_RICHNESS_AUG	0.0056	0.0088
12	COVER_RICHNESS_JUN	0.0047	0.0030
13	LAT	0.0045	0.0012
14	PASTURE_FORESTAREA	0.0034	0.0111
15	COVER_TOTAL_AUG	0.0026	0.0113
16	ENV_SOILMOIS	0.0025	0.0121
17	ENV_SOILNO3	0.0024	0.0004
18	ENV_AIRTEMP	0.0021	0.0040
19	COVER_SHANNON_JUL	0.0017	0.0013
20	PASTURE_WETLANDAREA	0.0016	0.0047
21	COVER_SHANNON_AUG	0.0014	0.0079
22	ENV_SOILCLAY	0.0002	0.0030
23	ENV_SOILNH4	-0.0000	0.0067
24	ENV_PH	-0.0003	0.0003
25	ENV_SOILTN	-0.0012	0.0024
26	ENV_SOILP	-0.0013	0.0192
27	ENV_SOILOC	-0.0025	0.0061
28	ENV_PROBEMOIS_AUG	-0.0043	0.0407
29	ENV_SOILTC	-0.0045	0.0122
30	ENV_PROBEMOIS_JUL	-0.0061	0.0054
31	ENV_SOILSILT	-0.0144	0.0157
32	GH_COUNT	-0.0186	0.0150
33	ENV_SOILSAND	-0.0188	0.0247

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SHAP Feature Importance Help

Rank	Feature	SHAP Mean \|Abs\|
1	PC_HEIGHT	3.4051
2	COVER_TOTAL_JUL	1.0292
3	ENV_PROBEMOIS_AUG	0.7705
4	ENV_SOILMOIS	0.4773
5	COVER_TOTAL_AUG	0.4086
6	COVER_RICHNESS_AUG	0.3694
7	GH_COUNT	0.3585
8	PASTURE_TOTALAREA	0.3453
9	ENV_SOILTC	0.2734
10	COVER_TOTAL_JUN	0.2639
11	ENV_SOILP	0.2598
12	COVER_SHANNON_JUN	0.2401
13	ENV_SOILTN	0.2353
14	COVER_RICHNESS_JUL	0.2221
15	COVER_SHANNON_AUG	0.2190
16	SUBPASTURE_TOTALAREA	0.2145
17	ENV_SOILSAND	0.2045
18	ENV_SOILSILT	0.2038
19	LONG	0.2035
20	COVER_SHANNON_JUL	0.1782
21	COVER_RICHNESS_JUN	0.1563
22	ENV_PH	0.1390
23	PASTURE_FORESTAREA	0.1346
24	ENV_AIRTEMP	0.1285
25	LAT	0.1261
26	ENV_SOILOC	0.1207
27	PLOT	0.1206
28	ENV_PROBEMOIS_JUL	0.1067
29	ENV_SOILNO3	0.0887
30	ENV_PROBEMOIS_JUN	0.0873
31	ENV_SOILNH4	0.0826
32	PASTURE_WETLANDAREA	0.0630
33	ENV_SOILCLAY	0.0579

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Model Metrics

Split	R²	MSE
Split 1	0.5417	54.2708
Split 2	0.3368	47.8353
Split 3	0.1707	50.4267
Mean	0.3497	50.8443

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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 #141)
Engine: Python · scikit-learn v1.8.0
Dataset(s): #695 gallery — catg_example_01_forage_biomass.csv (preprocessed)
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/f95ceea32876467a84d79698d140674d Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:13:18.142034

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)