Random Forest run #143 · random_forest

Table view Dataset report
-0.0492
r2 mean
regression
task type
0.02987
r2 std
7.228
mse mean
completed
status
2026-09-21 02:13
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
MEAN_PC_SHOOTMASS0.084806233256093020.0055835764884034425
MEAN_RC_SHOOTMASS0.130502400502062580.10491080772201626
MEAN_PC_HEIGHT0.0261027622619259370.019746532715409312
MEAN_ENV_SOILOC0.19878947979551530.07569811700916766
MEAN_ENV_SOILMOIS0.088198912687765420.11461765315974548
MEAN_ENV_SOILTN0.328191555887555550.01485950860824889
MEAN_COVER_TOTAL_JUL0.132638024727117280.06745830279285273
MEAN_COVER_SHANNON_JUL-0.0279030902798946720.031621418836514224
MEAN_PC_WOODMASS-0.056028437709616580.025227696810781124
PLOT_COUNT-0.048951288950545880.05550496356485122
Shap Importance 10 rows
namemean_abs
MEAN_PC_SHOOTMASS0.26527505652578615
MEAN_RC_SHOOTMASS0.3050425059001949
MEAN_PC_HEIGHT0.11223684490794007
MEAN_ENV_SOILOC0.10894809319326226
MEAN_ENV_SOILMOIS0.22500835505968333
MEAN_ENV_SOILTN0.3429474158423432
MEAN_COVER_TOTAL_JUL0.1732610112125007
MEAN_COVER_SHANNON_JUL0.08541102087753144
MEAN_PC_WOODMASS0.2287456973306408
PLOT_COUNT0.009979080932784596
Split Metrics 2 rows
r2mse
-0.079066659552411217.433570321361054
-0.019324154928147547.022010845060571
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #143)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#697 gallery — catg_example_02_grazing_carrying_capacity.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/c2f085d2764a43e3b8a578284d15edb5 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:13:52.593069

Parameters used

ParameterValue
task_type regression
model_type regression
target_column CW_NUMBER
n_estimators 60
n_splits 2
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=2)