Random Forest run #145 · random_forest

Table view Dataset report
0.7844
r2 mean
regression
task type
0.02711
r2 std
0.02856
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
PLOT0.00172761265203993340.0019221552294313542
ENV_SOILNH40.00103801746607773050.00145390876771763
ENV_SOILNO3-0.00082714561560668360.0014475855073844072
ENV_SOILP-0.00143919216789559390.0018332588424028152
ENV_SOILOC0.00201927546409513480.0007534498881985199
ENV_SOILTN-0.000309014804574891930.0007563487057241571
ENV_SOILTC-0.000262519302824211950.0010782891811643462
ENV_PH0.00101345554734930660.0009992335170476803
ENV_SOILCLAY-0.00145172203757070170.003740263636609074
ENV_SOILSILT1.1837891625254823e-050.0012757113282568825
Shap Importance 10 rows
namemean_abs
PLOT0.0025439090230564847
ENV_SOILNH40.0018688880142199151
ENV_SOILNO30.002629417055526704
ENV_SOILP0.0027380436645653367
ENV_SOILOC0.002754212589314421
ENV_SOILTN0.001205612143755236
ENV_SOILTC0.001763452756766477
ENV_PH0.006020888178945097
ENV_SOILCLAY0.0029913332176654886
ENV_SOILSILT0.002607006133837812
Split Metrics 3 rows
r2mse
0.74612444699765650.030228294884336433
0.80457032370709450.026807234754883384
0.80263873332870860.028641387737986294
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #145)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#699 gallery — catg_example_04_vegetation_diversity.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/023c0e89aaf94a488763f77c20de38a3 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:14:42.722532

Parameters used

ParameterValue
task_type regression
model_type regression
target_column COVER_SHANNON_JUL
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)