Random Forest Result #150 — Analysis Report
Contents
- random forest (#150) completed
1. Random Forest
2026-09-21 02:16:24 · random_forest
The classifier predicts the target with 95.6% cross-validated accuracy, weighted F1 0.955.
| task_type | classification |
|---|---|
| accuracy_mean | 0.955556 |
| accuracy_std | 0.031427 |
| f1_mean | 0.955079 |
| f1_std | 0.031769 |
| n_classes | 3 |
permutation importance
| name | mean | std |
|---|---|---|
| f1 | 0.538889 | 0.053495 |
| f2 | 0.123333 | 0.054637 |
| f3 | -0.015556 | 0.013699 |
shap importance
| name | mean_abs |
|---|---|
| f1 | 0.248198 |
| f2 | 0.131247 |
| f3 | 0.033876 |
split metrics
| accuracy | f1_weighted |
|---|---|
| 0.933333 | 0.931905 |
| 0.933333 | 0.933333 |
| 1.0 | 1.0 |
importance bar
permutation bar
shap summary
confusion heatmap
cv boxplot
Method & Provenance
- Method
- Random Forest (Classification)
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — example_classification.csv
- Started
- 2026-09-21 02:16:24.643806
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/75fc6b424d704db4b2f7621b482e84b5
| Parameter | Value |
|---|---|
| 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 |
Command history
# python from sklearn.ensemble import RandomForestClassifier # python model = RandomForestClassifier(n_estimators=60, random_state=42) # python scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)