Random Forest run #140 · random_forest

Table view Dataset report
1
accuracy mean
classification
task type
0
accuracy std
1
f1 mean
completed
status
2026-09-21 02:12
created
Figures
Feature importance Download PNG
Feature importance
Permutation importance Download PNG
Permutation importance
SHAP summary Download PNG
SHAP summary
Confusion matrix Download PNG
Confusion matrix
Cross-validation score distribution Download PNG
Cross-validation score distribution
Permutation Importance 10 rows
namemeanstd
PLOT0.00.0
LAT0.00466666666666667050.00659966329107445
LONG0.011000000000000010.004966554808583784
ENV_SOILNH40.00.0
ENV_SOILNO30.00.0
ENV_SOILP0.00.0
ENV_SOILOC0.00466666666666667050.00659966329107445
ENV_SOILTN0.00.0
ENV_SOILTC0.00.0
ENV_PH0.00.0
Shap Importance 10 rows
namemean_abs
PLOT0.06624841657564336
LAT0.0263298841482244
LONG0.07788833710028199
ENV_SOILNH40.0
ENV_SOILNO30.0003311214311087085
ENV_SOILP0.0
ENV_SOILOC0.013416222784055624
ENV_SOILTN0.013949921217256283
ENV_SOILTC0.013065768612241412
ENV_PH0.0
Split Metrics 3 rows
accuracyf1_weighted
1.01.0
1.01.0
1.01.0
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Method & Provenance

Method
Random Forest (Classification)
Method key
random_forest (result type: random_forest, result #140)
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/76d644e54c38460abc9ca6f21474ff98 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:12:05.989988

Parameters used

ParameterValue
task_type classification
model_type classification
target_column RANCH
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 RandomForestClassifier
model = RandomForestClassifier(n_estimators=60, random_state=42)
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)