Random Forest run #147 · random_forest

Table view Dataset report
0.8353
r2 mean
regression
task type
0.04353
r2 std
0.7677
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
PLOT0.0117389276495215790.01802171648633947
ENV_SOILNH4-0.00198968404920833640.0033672568270056947
ENV_SOILNO30.00199930858915063150.0008214013036493047
ENV_SOILP-0.00175223410830498770.0014914085151265704
ENV_SOILTN0.064513613579703130.015945570391334634
ENV_SOILTC0.1352188777312530.012696135120191519
ENV_PH0.0027619163268577640.0008744487977991743
ENV_SOILCLAY0.00177618802098985890.004231567392081222
ENV_SOILSILT0.073887468236289420.018645265350723
ENV_SOILSAND0.0224806681670588930.009010076594767375
Shap Importance 10 rows
namemean_abs
PLOT0.01682453199049442
ENV_SOILNH40.021394795044627046
ENV_SOILNO30.011123752159388342
ENV_SOILP0.009139737672909543
ENV_SOILTN0.7021796561817918
ENV_SOILTC0.7277512423203538
ENV_PH0.018505720789132835
ENV_SOILCLAY0.012148119957292808
ENV_SOILSILT0.11380445553515985
ENV_SOILSAND0.07488552049897956
Split Metrics 3 rows
r2mse
0.8679468310171970.6966697494411558
0.86424196049243810.6185894524547539
0.77381724849400160.9878696474472416
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #147)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#701 gallery — catg_example_06_soil_carbon_credits.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/0af7a5128668442fb3b9c296168aaf7d Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:15:26.727584

Parameters used

ParameterValue
task_type regression
model_type regression
target_column ENV_SOILOC
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)