Random Forest run #146 · random_forest

Table view Dataset report
0.7718
r2 mean
regression
task type
0.041
r2 std
199.7
mse mean
completed
status
2026-09-21 02:15
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
PLOT0.00076249313280760680.008947894884747594
ENV_SOILNH4-0.00041997515841938330.0020215511396638876
ENV_SOILNO3-0.000453936526273035570.004076804946890784
ENV_SOILP0.0045550098168278080.0028795242880662935
ENV_SOILOC0.0025015061525366220.004560962068618788
ENV_SOILTN0.0169416249941444770.011589357730139152
ENV_SOILTC0.0181217421876618960.009579876955403516
ENV_PH-0.000351930512162742150.00036441105254836337
ENV_SOILCLAY-0.000107877994259586420.0008761841304559256
ENV_SOILSILT0.0058316766925670.0018238249538403489
Shap Importance 10 rows
namemean_abs
PLOT0.4710796847938855
ENV_SOILNH40.21568695321668938
ENV_SOILNO30.1361839678785033
ENV_SOILP0.35094607985013226
ENV_SOILOC0.8542073591021017
ENV_SOILTN0.3581740098618065
ENV_SOILTC0.5716769658739185
ENV_PH0.2142360956967478
ENV_SOILCLAY0.29941365636819595
ENV_SOILSILT0.2901856597340872
Split Metrics 3 rows
r2mse
0.7458559065192978234.19729675224457
0.7398378332198586198.47763103254775
0.8296751396742852166.45678352974193
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Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #146)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#700 gallery — catg_example_05_grasshopper_pest_pressure.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/41da97babc1945769dd1bb43f3a17845 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:15:02.446843

Parameters used

ParameterValue
task_type regression
model_type regression
target_column GH_COUNT
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)