Random Forest Result #147 — Analysis Report
Contents
- random forest (#147) completed
1. Random Forest
2026-09-21 02:15:26 · random_forest
The model explains 83.5% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.835335 |
| r2_std | 0.043526 |
| mse_mean | 0.76771 |
| mse_std | 0.158907 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.011739 | 0.018022 |
| ENV_SOILNH4 | -0.00199 | 0.003367 |
| ENV_SOILNO3 | 0.001999 | 0.000821 |
| ENV_SOILP | -0.001752 | 0.001491 |
| ENV_SOILTN | 0.064514 | 0.015946 |
| ENV_SOILTC | 0.135219 | 0.012696 |
| ENV_PH | 0.002762 | 0.000874 |
| ENV_SOILCLAY | 0.001776 | 0.004232 |
| ENV_SOILSILT | 0.073887 | 0.018645 |
| ENV_SOILSAND | 0.022481 | 0.00901 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.016825 |
| ENV_SOILNH4 | 0.021395 |
| ENV_SOILNO3 | 0.011124 |
| ENV_SOILP | 0.00914 |
| ENV_SOILTN | 0.70218 |
| ENV_SOILTC | 0.727751 |
| ENV_PH | 0.018506 |
| ENV_SOILCLAY | 0.012148 |
| ENV_SOILSILT | 0.113804 |
| ENV_SOILSAND | 0.074886 |
split metrics
| r2 | mse |
|---|---|
| 0.867947 | 0.69667 |
| 0.864242 | 0.618589 |
| 0.773817 | 0.98787 |
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 — catg_example_06_soil_carbon_credits.csv
- Started
- 2026-09-21 02:15:26.727584
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/0af7a5128668442fb3b9c296168aaf7d
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | ENV_SOILOC |
| 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)