Random Forest run #144 · random_forest

Table view Dataset report
0.5388
r2 mean
regression
task type
0.2142
r2 std
14.99
mse mean
completed
status
2026-09-21 02:14
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
PLOT0.0112723974689055150.008572213498879985
ENV_SOILOC0.0012345867345011570.00782522390614005
ENV_SOILTC0.0025767453976586570.011295704087572363
ENV_SOILTN0.102671058370691830.07068502372160936
ENV_PH-0.00283812289870703650.002858231513095173
ENV_SOILCLAY0.0144503740689791370.009704035220638651
ENV_SOILSILT0.00640643022334361650.02344490066654397
ENV_SOILSAND0.010353252603945580.007540182123754726
ENV_SOILMOIS0.021318771277945630.01891356550336117
ENV_SOILP0.000114104804413935490.000990892933583054
Shap Importance 10 rows
namemean_abs
PLOT0.1130996378634109
ENV_SOILOC0.1836594395479482
ENV_SOILTC0.1262294267446615
ENV_SOILTN0.3270071017266399
ENV_PH0.1097968091584998
ENV_SOILCLAY0.06133893124863505
ENV_SOILSILT0.1672477933245032
ENV_SOILSAND0.06562169390007602
ENV_SOILMOIS0.20568244242581402
ENV_SOILP0.031209670726313223
Split Metrics 3 rows
r2mse
0.3083968393664915.958141417587487
0.483594771266458456.311223198755485
0.824277461932301332.69107878727193
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #144)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#698 gallery — catg_example_03_soil_nitrogen_availability.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/413ee895cd9742ed95bd1502567c309a Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:14:14.068246

Parameters used

ParameterValue
task_type regression
model_type regression
target_column ENV_SOILNH4
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)