Random Forest run #101 · ml_result

Table view Dataset report
regression
task type
random_forest
model type
0.2719
r2 mean
0.01656
r2 std
completed
status
2026-09-21 02:12
created
Figures
Feature importance Download PNG
Feature importance
Cross-validation score distribution Download PNG
Cross-validation score distribution
Pipeline Stages 3 rows
stagedetail
preprocessingmissing values: mean
feature_selectionkbest_f (k=5); 40 → 5 features
modelrandom_forest (regression), 3-fold cross-validation
Feature Importance 5 rows
namevalue
ENV_SOILOC0.17793142045738233
ENV_SOILTN0.13315263001126423
ENV_SOILTC0.12896309611900642
ENV_SOILMOIS0.1542793735085912
RC_SHOOTMASS0.4056734799037558
Split Metrics 3 rows
r2mse
0.2948847057630016365.11308921118005
0.264187356271766869.04177119580399
0.2565869673214633655.71141053536753
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Method & Provenance

Method
Random Forest
Method key
ml_result (result type: ml_result, result #101)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#690 gallery — cat_g_field_data_plots.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/a316fb7a42834890a899475de981b6f9 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:12:52.641122

Parameters used

ParameterValue
model_type random_forest
target_column PC_SHOOTMASS
n_splits 3

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made. 

# Models compared via app.services.ml_model_registry
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)