Random Forest run #141 · random_forest

Table view Dataset report
0.3497
r2 mean
regression
task type
0.1517
r2 std
50.84
mse mean
completed
status
2026-09-21 02:13
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
PLOT0.0091368276028348640.004064594892207052
ENV_SOILNH4-2.9125000989112363e-050.006681635724583668
ENV_SOILNO30.00238907608892897270.00039139971378304364
ENV_SOILP-0.00128721894929729090.019212564898874202
ENV_SOILOC-0.0024922525295022890.006131120790839137
ENV_SOILTN-0.00121797592235185490.002394844059952514
ENV_SOILTC-0.0045293129857142770.012191572834977854
ENV_PH-0.00025977098490355490.00026905657096458935
ENV_SOILCLAY0.00024855675531509550.0029598958307384634
ENV_SOILSILT-0.0143570476449019070.01571392611111486
Shap Importance 10 rows
namemean_abs
PLOT0.12061470388682435
ENV_SOILNH40.08263559944166099
ENV_SOILNO30.08872209261136968
ENV_SOILP0.25975595701280135
ENV_SOILOC0.12071047192457554
ENV_SOILTN0.23525964284095488
ENV_SOILTC0.273366953073277
ENV_PH0.13898826796867797
ENV_SOILCLAY0.05794058459171843
ENV_SOILSILT0.203844873670462
Split Metrics 3 rows
r2mse
0.541696126339147354.27076571940582
0.336801531823497647.835304417255905
0.170674223912737850.42671884914353
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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

ParameterValue
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)