Random Forest run #138 · random_forest

Table view Dataset report
0.5441
r2 mean
regression
task type
0.01283
r2 std
39.37
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
PLOT0.0003064104643982620.0007241751530180119
LAT0.0077319044171017720.0017744500031876618
LONG0.00150283088667735320.0015403182322793756
ENV_SOILNH40.00362866605468736450.0027430781363809906
ENV_SOILNO3-0.00059412669510407290.002810987433764491
ENV_SOILP0.0009280099820119790.002479925503249013
ENV_SOILOC0.0128803745810401740.008753076625029662
ENV_SOILTN0.0116574046244313280.002143107396613919
ENV_SOILTC0.0041597827524577150.007554996314746065
ENV_PH-0.0029563673812469310.0033304519807646977
Shap Importance 10 rows
namemean_abs
PLOT0.08638531883825801
LAT0.2413933900127435
LONG0.05058189380871614
ENV_SOILNH40.09908980699466952
ENV_SOILNO30.068238960668425
ENV_SOILP0.0552369688347661
ENV_SOILOC0.10404332325897332
ENV_SOILTN0.7207279367723096
ENV_SOILTC0.30083698187909885
ENV_PH0.17397710069465802
Split Metrics 3 rows
r2mse
0.559827083081276644.48655267075641
0.528399272980635739.07231074959226
0.544211314120423534.55140908003043
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #138)
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/e38fdc04752c42c5a01d7772f67245f3 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:11:08.707331

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)