Random Forest Result #151 — Analysis Report
Contents
- random forest (#151) completed
1. Random Forest
2026-09-21 02:17:48 · random_forest
The model explains 0.0% of the variance in the target — worse than simply predicting the mean; treat predictions as unreliable.
| task_type | regression |
|---|---|
| r2_mean | -0.5783 |
| r2_std | 0.617272 |
| mse_mean | 3.519505 |
| mse_std | 1.086945 |
permutation importance
| name | mean | std |
|---|---|---|
| Protein_A01 | 0.013745 | 0.033767 |
| Protein_B02 | -0.014693 | 0.036506 |
| Protein_C03 | 0.168846 | 0.143659 |
| Protein_D04 | -0.01372 | 0.008411 |
| Protein_E05 | -0.082587 | 0.089068 |
| Protein_F06 | 0.194496 | 0.264982 |
| Protein_G07 | 0.114167 | 0.171214 |
| Protein_H08 | 0.008102 | 0.009232 |
| Protein_I09 | 0.192246 | 0.164957 |
| Protein_J10 | 0.171206 | 0.182525 |
shap importance
| name | mean_abs |
|---|---|
| Protein_A01 | 0.104572 |
| Protein_B02 | 0.036776 |
| Protein_C03 | 0.247165 |
| Protein_D04 | 0.018802 |
| Protein_E05 | 0.030457 |
| Protein_F06 | 0.434281 |
| Protein_G07 | 0.218849 |
| Protein_H08 | 0.023725 |
| Protein_I09 | 0.248877 |
| Protein_J10 | 0.263879 |
split metrics
| r2 | mse |
|---|---|
| -0.958022 | 4.947417 |
| 0.292293 | 3.298471 |
| -1.069172 | 2.312626 |
importance bar
permutation bar
shap summary
pred vs obs
cv boxplot
Method & Provenance
- Method
- Random Forest (Regression)
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — example_proteomics.csv
- Started
- 2026-09-21 02:17:48.827397
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/a55dc899cd514343aea98c71daeb65ee
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | Protein_content |
| 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 RandomForestRegressor # python model = RandomForestRegressor(n_estimators=60, random_state=42) # python scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)