Random Forest run #142 · random_forest

Table view Dataset report
0.3456
r2 mean
regression
task type
0.123
r2 std
52.23
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
PLOT0.0211275037437624880.013874511002845645
ENV_SOILNH40.00180177130306802270.001265011479115152
ENV_SOILNO30.00336221709374080530.00332869848352927
ENV_SOILP0.0164945245304651370.003533667499608135
ENV_SOILOC0.009075668124047960.015999583611377075
ENV_SOILTN0.0090669031204678760.012805093917287649
ENV_SOILTC0.0142877693996299930.047237425545886204
ENV_PH0.00124940268401009630.0029388747298724283
ENV_SOILCLAY0.00331378408903821140.004582916074870132
ENV_SOILSILT-0.0057898289299398190.007295478740859575
Shap Importance 10 rows
namemean_abs
PLOT0.24763917378500644
ENV_SOILNH40.10329568688488226
ENV_SOILNO30.09458471502369745
ENV_SOILP0.2171717055673165
ENV_SOILOC0.2923395256337868
ENV_SOILTN0.7821528688793861
ENV_SOILTC0.7652647199757844
ENV_PH0.15605390090319554
ENV_SOILCLAY0.06528010523891664
ENV_SOILSILT0.18887117494162253
Split Metrics 3 rows
r2mse
0.472938135905532762.41285005387343
0.384565963398185744.39014247203212
0.1794119722842626449.895424642198385
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #142)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#694 gallery — catg_example_01_forage_biomass.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/96b7753d8e314763a196bb620423f685 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:13:38.003816

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)