Result Detail – Random Forest

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Possible data leakage detected:

Feature "FUNGAL_NECROMASS_C" is almost perfectly correlated with the target "MICROBIAL_NECROMASS_C" (r = 0.997) — likely data leakage or a duplicate measurement; consider excluding it.
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.

Feature	Pearson r	Pearson p	Pearson FDR	Spearman ρ	Spearman FDR	ANOVA F	ANOVA FDR	Significant?
PLOT	-0.8094	0.0000	0.0000	-0.7607	0.0000	178.5728	0.0000	✓ Yes
ENV_SOILNH4	0.0826	0.4235	0.4941	0.2660	0.0123	0.6462	0.4941	✓ Yes
ENV_SOILNO3	0.0429	0.6778	0.7492	-0.0032	0.9752	0.1737	0.7492	✗ No
ENV_SOILP	0.1838	0.0730	0.0902	0.1593	0.1412	3.2876	0.0902	✗ No
ENV_SOILOC	0.8337	0.0000	0.0000	0.8261	0.0000	214.2935	0.0000	✓ Yes
ENV_SOILTN	0.8292	0.0000	0.0000	0.8689	0.0000	206.9389	0.0000	✓ Yes
ENV_SOILTC	0.8201	0.0000	0.0000	0.8628	0.0000	193.0784	0.0000	✓ Yes
ENV_PH	-0.1949	0.0570	0.0798	-0.1089	0.3217	3.7128	0.0798	✗ No
ENV_SOILCLAY	0.7880	0.0000	0.0000	0.7779	0.0000	153.9446	0.0000	✓ Yes
ENV_SOILSILT	0.8273	0.0000	0.0000	0.8399	0.0000	203.9462	0.0000	✓ Yes
ENV_SOILSAND	-0.8430	0.0000	0.0000	-0.8509	0.0000	230.8978	0.0000	✓ Yes
ENV_SOILMOIS	0.7658	0.0000	0.0000	0.7992	0.0000	133.3222	0.0000	✓ Yes
PC_SHOOTMASS	0.4026	0.0000	0.0001	0.4904	0.0000	18.1837	0.0001	✓ Yes
PC_ROOTMASS	0.0207	0.8414	0.8414	0.1807	0.0964	0.0403	0.8414	✗ No
PC_LITTERMASS	0.1888	0.0655	0.0859	0.2578	0.0147	3.4742	0.0859	✓ Yes
COVER_TOTAL_JUL	0.2255	0.0272	0.0408	0.3868	0.0001	5.0362	0.0408	✓ Yes
COVER_SHANNON_JUL	0.0272	0.7927	0.8323	0.0335	0.7831	0.0695	0.8323	✗ No
SOIL_EC_uS_cm	-0.5998	0.0000	0.0000	-0.7424	0.0000	52.8183	0.0000	✓ Yes
SOIL_CO2_mg_per_g	0.5712	0.0000	0.0000	0.7227	0.0000	45.5107	0.0000	✓ Yes
BACTERIAL_NECROMASS_C	0.8041	0.0000	0.0000	0.8837	0.0000	172.0237	0.0000	✓ Yes
FUNGAL_NECROMASS_C	0.9974	0.0000	0.0000	0.9885	0.0000	17705.0452	0.0000	✓ Yes

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Rank	Feature	Importance (Mean)	SD
1	FUNGAL_NECROMASS_C	0.9260	0.0212
2	ENV_SOILSAND	0.0140	0.0118
3	ENV_SOILCLAY	0.0131	0.0070
4	ENV_SOILSILT	0.0068	0.0012
5	ENV_SOILTN	0.0063	0.0063
6	ENV_SOILMOIS	0.0060	0.0005
7	PLOT	0.0044	0.0014
8	ENV_SOILNH4	0.0043	0.0030
9	ENV_SOILTC	0.0038	0.0003
10	BACTERIAL_NECROMASS_C	0.0037	0.0021
11	ENV_SOILP	0.0024	0.0011
12	SOIL_CO2_mg_per_g	0.0016	0.0011
13	SOIL_EC_uS_cm	0.0015	0.0009
14	ENV_SOILOC	0.0013	0.0011
15	PC_SHOOTMASS	0.0012	0.0002
16	ENV_SOILNO3	0.0009	0.0005
17	COVER_SHANNON_JUL	0.0009	0.0008
18	COVER_TOTAL_JUL	0.0007	0.0003
19	ENV_PH	0.0004	0.0001
20	PC_LITTERMASS	0.0004	0.0000
21	PC_ROOTMASS	0.0001	0.0000

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Rank	Feature	Perm. Importance (Mean)	SD
1	FUNGAL_NECROMASS_C	1.4052	0.2257
2	ENV_SOILSAND	0.0032	0.0009
3	ENV_SOILTN	0.0023	0.0022
4	ENV_SOILCLAY	0.0020	0.0004
5	ENV_SOILMOIS	0.0020	0.0008
6	PLOT	0.0008	0.0013
7	ENV_SOILSILT	0.0007	0.0014
8	BACTERIAL_NECROMASS_C	0.0006	0.0011
9	ENV_SOILTC	0.0004	0.0002
10	SOIL_EC_uS_cm	0.0002	0.0004
11	COVER_TOTAL_JUL	0.0002	0.0002
12	COVER_SHANNON_JUL	0.0001	0.0002
13	ENV_SOILNO3	0.0001	0.0004
14	SOIL_CO2_mg_per_g	0.0001	0.0001
15	PC_SHOOTMASS	0.0001	0.0005
16	ENV_SOILOC	0.0000	0.0003
17	PC_ROOTMASS	0.0000	0.0000
18	ENV_PH	-0.0001	0.0002
19	PC_LITTERMASS	-0.0002	0.0000
20	ENV_SOILP	-0.0005	0.0008
21	ENV_SOILNH4	-0.0031	0.0041

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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

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Method & Provenance

Method: Random Forest (Regression)
Method key: random_forest (result type: random_forest, result #148)
Engine: Python · scikit-learn v1.8.0
Dataset(s): #702 gallery — catg_example_07_microbial_soil_biology.csv
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/8705c62fa577496199d0d6e6fc4f47a1 Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:15:49.155216

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)