Result Detail – Random Forest Help

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Result Information

Result ID: 146
Type: Random Forest
Status: completed
Dataset ID: 700
Created: 2026-09-21 02:15:02.446843
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.
Spatial confounding risk: coordinate column(s) LAT, LONG rank among the most important features. The model may be learning field position rather than biological signal; interpret with care or exclude coordinates.

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	0.7889	0.0000	0.0000	0.7476	0.0000	359.3138	0.0000	✓ Yes
ENV_SOILNH4	-0.0426	0.5301	0.6058	-0.1940	0.0059	0.3955	0.6058	✓ Yes
ENV_SOILNO3	-0.0199	0.7690	0.7875	-0.0766	0.3054	0.0864	0.7875	✗ No
ENV_SOILP	-0.1793	0.0077	0.0112	-0.1434	0.0466	7.2381	0.0112	✓ Yes
ENV_SOILOC	-0.6560	0.0000	0.0000	-0.6859	0.0000	164.6414	0.0000	✓ Yes
ENV_SOILTN	-0.6862	0.0000	0.0000	-0.7478	0.0000	194.0268	0.0000	✓ Yes
ENV_SOILTC	-0.6836	0.0000	0.0000	-0.7480	0.0000	191.2160	0.0000	✓ Yes
ENV_PH	0.0183	0.7875	0.7875	-0.0116	0.9216	0.0729	0.7875	✗ No
ENV_SOILCLAY	-0.6971	0.0000	0.0000	-0.6572	0.0000	206.0559	0.0000	✓ Yes
ENV_SOILSILT	-0.7121	0.0000	0.0000	-0.6923	0.0000	224.2286	0.0000	✓ Yes
ENV_SOILSAND	0.7357	0.0000	0.0000	0.6805	0.0000	257.2186	0.0000	✓ Yes
ENV_SOILMOIS	-0.6759	0.0000	0.0000	-0.6762	0.0000	183.3840	0.0000	✓ Yes
COVER_RICHNESS_JUN	-0.0368	0.5875	0.6482	0.0243	0.7948	0.2952	0.6482	✗ No
COVER_TOTAL_JUN	-0.3482	0.0000	0.0000	-0.4848	0.0000	30.0740	0.0000	✓ Yes
COVER_SHANNON_JUN	-0.0202	0.7657	0.7875	0.0458	0.5702	0.0890	0.7875	✗ No
COVER_RICHNESS_JUL	-0.0551	0.4159	0.5119	-0.0081	0.9345	0.6643	0.5119	✗ No
COVER_TOTAL_JUL	-0.3212	0.0000	0.0000	-0.4040	0.0000	25.0791	0.0000	✓ Yes
COVER_SHANNON_JUL	-0.0446	0.5108	0.6054	0.0032	0.9620	0.4339	0.6054	✗ No
COVER_RICHNESS_AUG	-0.1298	0.0546	0.0752	-0.0795	0.2959	3.7330	0.0752	✗ No
COVER_TOTAL_AUG	-0.3896	0.0000	0.0000	-0.4391	0.0000	39.0059	0.0000	✓ Yes
COVER_SHANNON_AUG	-0.1250	0.0642	0.0821	-0.0871	0.2533	3.4610	0.0821	✗ No
ENV_AIRTEMP	0.1288	0.0564	0.0752	0.1291	0.0744	3.6786	0.0752	✗ No
PC_SHOOTMASS	-0.4104	0.0000	0.0000	-0.4546	0.0000	44.1448	0.0000	✓ Yes
PC_WOODMASS	-0.2104	0.0017	0.0026	-0.1848	0.0087	10.0997	0.0026	✓ Yes
PC_HEIGHT	-0.4866	0.0000	0.0000	-0.5260	0.0000	67.6302	0.0000	✓ Yes
PASTURE_TOTALAREA	0.7460	0.0000	0.0000	0.7106	0.0000	273.5428	0.0000	✓ Yes
PASTURE_FORESTAREA	-0.6601	0.0000	0.0000	-0.6574	0.0000	168.3847	0.0000	✓ Yes
LAT	-0.7813	0.0000	0.0000	-0.7662	0.0000	341.6048	0.0000	✓ Yes
LONG	-0.7812	0.0000	0.0000	-0.7097	0.0000	341.3497	0.0000	✓ Yes
ENV_PROBEMOIS_AUG	-0.7407	0.0000	0.0000	-0.7882	0.0000	264.9153	0.0000	✓ Yes
ENV_PROBEMOIS_JUL	-0.6653	0.0000	0.0000	-0.6787	0.0000	173.1313	0.0000	✓ Yes
ENV_PROBEMOIS_JUN	-0.4157	0.0000	0.0000	-0.5135	0.0000	45.5412	0.0000	✓ Yes

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Feature Importance (Random Forest) Help

Rank	Feature	Importance (Mean)	SD
1	LONG	0.5986	0.0720
2	LAT	0.1091	0.0329
3	ENV_PROBEMOIS_AUG	0.0483	0.0380
4	ENV_SOILMOIS	0.0307	0.0076
5	ENV_PROBEMOIS_JUN	0.0238	0.0120
6	ENV_SOILTN	0.0237	0.0066
7	ENV_SOILTC	0.0214	0.0083
8	PC_HEIGHT	0.0097	0.0018
9	COVER_SHANNON_AUG	0.0091	0.0041
10	ENV_SOILSAND	0.0088	0.0016
11	COVER_TOTAL_AUG	0.0084	0.0031
12	PASTURE_FORESTAREA	0.0084	0.0063
13	PLOT	0.0080	0.0023
14	ENV_SOILOC	0.0079	0.0065
15	ENV_AIRTEMP	0.0075	0.0023
16	ENV_SOILP	0.0074	0.0010
17	PC_SHOOTMASS	0.0068	0.0021
18	ENV_SOILNH4	0.0067	0.0008
19	ENV_PROBEMOIS_JUL	0.0066	0.0018
20	ENV_SOILSILT	0.0064	0.0017
21	ENV_SOILNO3	0.0064	0.0016
22	COVER_TOTAL_JUN	0.0057	0.0033
23	ENV_SOILCLAY	0.0056	0.0040
24	COVER_TOTAL_JUL	0.0044	0.0004
25	COVER_RICHNESS_AUG	0.0038	0.0008
26	ENV_PH	0.0035	0.0004
27	COVER_SHANNON_JUN	0.0031	0.0005
28	COVER_RICHNESS_JUL	0.0030	0.0019
29	COVER_SHANNON_JUL	0.0029	0.0005
30	PC_WOODMASS	0.0015	0.0002
31	PASTURE_TOTALAREA	0.0015	0.0005
32	COVER_RICHNESS_JUN	0.0013	0.0010

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Permutation Importance Help

Rank	Feature	Perm. Importance (Mean)	SD
1	LONG	0.6961	0.1163
2	LAT	0.1350	0.0843
3	ENV_SOILMOIS	0.0182	0.0154
4	ENV_SOILTC	0.0181	0.0096
5	ENV_SOILTN	0.0169	0.0116
6	ENV_PROBEMOIS_JUN	0.0142	0.0130
7	ENV_PROBEMOIS_AUG	0.0140	0.0142
8	ENV_SOILSAND	0.0121	0.0001
9	PC_HEIGHT	0.0082	0.0030
10	COVER_TOTAL_AUG	0.0060	0.0029
11	ENV_SOILSILT	0.0058	0.0018
12	ENV_SOILP	0.0046	0.0029
13	PASTURE_FORESTAREA	0.0031	0.0051
14	ENV_AIRTEMP	0.0030	0.0013
15	ENV_SOILOC	0.0025	0.0046
16	ENV_PROBEMOIS_JUL	0.0019	0.0049
17	PC_SHOOTMASS	0.0018	0.0022
18	COVER_SHANNON_AUG	0.0016	0.0030
19	PASTURE_TOTALAREA	0.0015	0.0002
20	COVER_TOTAL_JUL	0.0008	0.0005
21	PLOT	0.0008	0.0089
22	COVER_TOTAL_JUN	0.0006	0.0027
23	COVER_SHANNON_JUN	0.0004	0.0017
24	COVER_SHANNON_JUL	0.0002	0.0011
25	ENV_SOILCLAY	-0.0001	0.0009
26	ENV_PH	-0.0004	0.0004
27	ENV_SOILNH4	-0.0004	0.0020
28	ENV_SOILNO3	-0.0005	0.0041
29	PC_WOODMASS	-0.0006	0.0009
30	COVER_RICHNESS_AUG	-0.0007	0.0013
31	COVER_RICHNESS_JUL	-0.0008	0.0015
32	COVER_RICHNESS_JUN	-0.0012	0.0011

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SHAP Feature Importance Help

Rank	Feature	SHAP Mean \|Abs\|
1	LONG	14.9013
2	LAT	6.1385
3	ENV_PROBEMOIS_AUG	2.9282
4	ENV_SOILMOIS	2.0237
5	ENV_SOILOC	0.8542
6	ENV_SOILSAND	0.7792
7	COVER_SHANNON_AUG	0.7482
8	COVER_TOTAL_AUG	0.7287
9	PC_HEIGHT	0.5997
10	ENV_SOILTC	0.5717
11	ENV_PROBEMOIS_JUN	0.4980
12	PLOT	0.4711
13	ENV_AIRTEMP	0.4619
14	ENV_PROBEMOIS_JUL	0.4527
15	ENV_SOILTN	0.3582
16	ENV_SOILP	0.3509
17	PC_SHOOTMASS	0.3218
18	COVER_RICHNESS_JUL	0.3162
19	ENV_SOILCLAY	0.2994
20	ENV_SOILSILT	0.2902
21	ENV_SOILNH4	0.2157
22	ENV_PH	0.2142
23	COVER_TOTAL_JUN	0.1863
24	COVER_RICHNESS_AUG	0.1792
25	PASTURE_TOTALAREA	0.1725
26	COVER_TOTAL_JUL	0.1568
27	ENV_SOILNO3	0.1362
28	COVER_SHANNON_JUL	0.1233
29	COVER_RICHNESS_JUN	0.1205
30	COVER_SHANNON_JUN	0.1070
31	PC_WOODMASS	0.0912
32	PASTURE_FORESTAREA	0.0445

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Model Metrics

Split	R²	MSE
Split 1	0.7459	234.1973
Split 2	0.7398	198.4776
Split 3	0.8297	166.4568
Mean	0.7718	199.7106

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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 #146)
Engine: Python · scikit-learn v1.8.0
Dataset(s): #700 gallery — catg_example_05_grasshopper_pest_pressure.csv
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/41da97babc1945769dd1bb43f3a17845 Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:15:02.446843

Parameters used

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