Random Forest run #139 · random_forest

Table view Dataset report
0.5345
r2 mean
regression
task type
0.02022
r2 std
40.12
mse mean
completed
status
2026-09-21 02:11
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
PLOT0.00110449982886869540.0017475740253168359
LAT0.00181015896252004750.0009567957223432068
LONG0.0027245957678525590.0022772969823228626
ENV_SOILNH40.00213434040324845440.001710047815798785
ENV_SOILNO30.00231109257599343070.0035326151922504294
ENV_SOILP0.00156574316708430290.0034986018536285702
ENV_SOILOC0.0160124425004529070.006552028745932267
ENV_SOILTN0.0111407452124514720.01221091658400486
ENV_SOILTC-0.00076235765594375750.006242686701757729
ENV_PH-0.00281925483180485130.005170826613859093
Shap Importance 10 rows
namemean_abs
PLOT0.09255109369168478
LAT0.199098536917173
LONG0.04719575228605971
ENV_SOILNH40.11170298506563452
ENV_SOILNO30.05062122226777082
ENV_SOILP0.06099027475198146
ENV_SOILOC0.2213866364340796
ENV_SOILTN0.7347063209404829
ENV_SOILTC0.2402667065703003
ENV_PH0.19221660196930848
Split Metrics 3 rows
r2mse
0.562705235266691344.27039626675485
0.516368252995624640.05214732517015
0.5244102359547236.05244491774237
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #139)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#691 gallery — cat_g_field_data_plots.csv (preprocessed)
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/4ae9de70c16e41858d39d893714e9650 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:11:43.291308

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)