Random Forest run #149 · random_forest

Table view Dataset report
-0.1277
r2 mean
regression
task type
0.4306
r2 std
3.421
mse mean
completed
status
2026-09-21 02:16
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
PLOT0.0163374551002981040.003928238117440637
ENV_SOILNH40.0041426167960149160.002694891088047901
ENV_SOILNO30.0079934076451609330.006055508021827808
ENV_SOILP0.0027546540352554550.0033603925696667545
ENV_SOILOC-0.0053163550867442550.003436581805053185
ENV_SOILTN-0.00065796921176491580.0038849928230520955
ENV_SOILTC-0.0059234129462245630.008818918928813263
ENV_PH0.0045896945376870820.008165643704548007
ENV_SOILCLAY0.0289214622352355240.009043384557865668
ENV_SOILSILT-0.0034065952898115760.006026130501415434
Shap Importance 10 rows
namemean_abs
PLOT0.07077318956292715
ENV_SOILNH40.019476275346460268
ENV_SOILNO30.02822716641883192
ENV_SOILP0.02073624214196772
ENV_SOILOC0.0049944434988576205
ENV_SOILTN0.0035691896741697885
ENV_SOILTC0.005338105472269505
ENV_PH0.026288260433386824
ENV_SOILCLAY0.10092195954875043
ENV_SOILSILT0.04450745085937232
Split Metrics 3 rows
r2mse
-0.73218273055723.698356792207491
0.111214266230363684.261812452648709
0.23791605238160732.3020744527013193
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #149)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#703 gallery — catg_example_08_woody_encroachment.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/c2d3ecce80614f57be4dc0be42e7fcdb Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:16:12.854253

Parameters used

ParameterValue
task_type regression
model_type regression
target_column PC_WOODMASS
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)