Random Forest run #150 · random_forest

Table view Dataset report
0.9556
accuracy mean
classification
task type
0.03143
accuracy std
0.9551
f1 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
Confusion matrix Download PNG
Confusion matrix
Cross-validation score distribution Download PNG
Cross-validation score distribution
Permutation Importance 3 rows
namemeanstd
f10.53888888888888890.05349512496538101
f20.123333333333333340.05463684823619667
f3-0.0155555555555555520.0136986977843755
Shap Importance 3 rows
namemean_abs
f10.2481975547594833
f20.13124670626946122
f30.03387624815296618
Split Metrics 3 rows
accuracyf1_weighted
0.93333333333333330.9319047619047619
0.93333333333333330.9333333333333333
1.01.0
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Method & Provenance

Method
Random Forest (Classification)
Method key
random_forest (result type: random_forest, result #150)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#704 gallery — example_classification.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/75fc6b424d704db4b2f7621b482e84b5 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:16:24.643806

Parameters used

ParameterValue
task_type classification
model_type classification
target_column target
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)