Random Forest run #148 · random_forest

Table view Dataset report
0.9356
r2 mean
regression
task type
0.05525
r2 std
32.91
mse mean
completed
status
2026-09-21 02:15
created
Figures
Feature importance Download PNG
Feature importance
Permutation importance Download PNG
Permutation importance
SHAP summary Download PNG
SHAP summary
Observed vs predicted (out-of-fold) Download PNG
Observed vs predicted (out-of-fold)
Cross-validation score distribution Download PNG
Cross-validation score distribution
Permutation Importance 10 rows
namemeanstd
PLOT0.0008288321115863490.0012822446304695347
ENV_SOILNH4-0.00314721568442572280.004053912793870449
ENV_SOILNO38.859494959124747e-050.00035382379306897287
ENV_SOILP-0.00050052903490502840.000818899364492379
ENV_SOILOC2.4107632631971882e-050.0002685643129822234
ENV_SOILTN0.002319668360295050.0022026099307725206
ENV_SOILTC0.000415923661901736660.00018133037444470398
ENV_PH-0.000125634333920813620.00015382321391614804
ENV_SOILCLAY0.001992437833195130.00042461091135096536
ENV_SOILSILT0.00071795383577527950.0013789375434021286
Shap Importance 10 rows
namemean_abs
PLOT0.1959522272925375
ENV_SOILNH40.10978971620399985
ENV_SOILNO30.06422672706236172
ENV_SOILP0.10092821717319456
ENV_SOILOC0.01729727603909788
ENV_SOILTN0.07771773662129054
ENV_SOILTC0.18598463248034533
ENV_PH0.02226041576607388
ENV_SOILCLAY0.5588993137383454
ENV_SOILSILT0.4158851980166474
Split Metrics 3 rows
r2mse
0.99060764626667913.0793923574613706
0.860047346565376273.61604858075772
0.956222479049176622.0456743236855
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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

Parameters used

ParameterValue
task_type regression
model_type regression
target_column MICROBIAL_NECROMASS_C
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)