Result Detail – Ml Result

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Notebook

Model	Accuracy Mean	Accuracy Std	F1 Mean	F1 Std
Logistic Regression	0.9500	0.0540	0.9506	0.0531
Random Forest	0.9500	0.0354	0.9502	0.0349
K-Nearest Neighbours	0.9500	0.0540	0.9506	0.0531

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Feature importance not available for this model.

Permutation importance was not included in this run.

SHAP analysis was not included in this run.

Feature	Pearson r	Pearson p	Pearson FDR	Spearman ρ	Spearman FDR	ANOVA F	ANOVA FDR	Significant?
f1	-0.4773	0.0000	0.0000	-0.4682	0.0000	34.8210	0.0000	✓ Yes
f2	0.3454	0.0001	0.0002	0.3592	0.0001	15.9873	0.0002	✓ Yes
f3	0.1152	0.2104	0.2104	0.1205	0.1898	1.5859	0.2104	✗ No

Model comparison Download PNG

Cross-validation score distribution Download PNG

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Method & Provenance

Method: Model Comparison
Method key: ml_result (result type: ml_result, result #102)
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/a5a1439572d3484fab634cd6bbefcccd Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:16:32.038299

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.

# task_type = 'classification'

# Models compared via app.services.ml_model_registry

scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)