Random Forest Result #141 — Analysis Report
Contents
- random forest (#141) completed
1. Random Forest
2026-09-21 02:13:18 · random_forest
The model explains 35.0% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.349724 |
| r2_std | 0.151744 |
| mse_mean | 50.844263 |
| mse_std | 2.643804 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.009137 | 0.004065 |
| ENV_SOILNH4 | -2.9e-05 | 0.006682 |
| ENV_SOILNO3 | 0.002389 | 0.000391 |
| ENV_SOILP | -0.001287 | 0.019213 |
| ENV_SOILOC | -0.002492 | 0.006131 |
| ENV_SOILTN | -0.001218 | 0.002395 |
| ENV_SOILTC | -0.004529 | 0.012192 |
| ENV_PH | -0.00026 | 0.000269 |
| ENV_SOILCLAY | 0.000249 | 0.00296 |
| ENV_SOILSILT | -0.014357 | 0.015714 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.120615 |
| ENV_SOILNH4 | 0.082636 |
| ENV_SOILNO3 | 0.088722 |
| ENV_SOILP | 0.259756 |
| ENV_SOILOC | 0.12071 |
| ENV_SOILTN | 0.23526 |
| ENV_SOILTC | 0.273367 |
| ENV_PH | 0.138988 |
| ENV_SOILCLAY | 0.057941 |
| ENV_SOILSILT | 0.203845 |
split metrics
| r2 | mse |
|---|---|
| 0.541696 | 54.270766 |
| 0.336802 | 47.835304 |
| 0.170674 | 50.426719 |
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_01_forage_biomass.csv (preprocessed)
- Started
- 2026-09-21 02:13:18.142034
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/f95ceea32876467a84d79698d140674d
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | PC_SHOOTMASS |
| 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)