Random Forest Result #142 — Analysis Report
Contents
- random forest (#142) completed
1. Random Forest
2026-09-21 02:13:38 · random_forest
The model explains 34.6% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.345639 |
| r2_std | 0.122952 |
| mse_mean | 52.232806 |
| mse_std | 7.541088 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.021128 | 0.013875 |
| ENV_SOILNH4 | 0.001802 | 0.001265 |
| ENV_SOILNO3 | 0.003362 | 0.003329 |
| ENV_SOILP | 0.016495 | 0.003534 |
| ENV_SOILOC | 0.009076 | 0.016 |
| ENV_SOILTN | 0.009067 | 0.012805 |
| ENV_SOILTC | 0.014288 | 0.047237 |
| ENV_PH | 0.001249 | 0.002939 |
| ENV_SOILCLAY | 0.003314 | 0.004583 |
| ENV_SOILSILT | -0.00579 | 0.007295 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.247639 |
| ENV_SOILNH4 | 0.103296 |
| ENV_SOILNO3 | 0.094585 |
| ENV_SOILP | 0.217172 |
| ENV_SOILOC | 0.29234 |
| ENV_SOILTN | 0.782153 |
| ENV_SOILTC | 0.765265 |
| ENV_PH | 0.156054 |
| ENV_SOILCLAY | 0.06528 |
| ENV_SOILSILT | 0.188871 |
split metrics
| r2 | mse |
|---|---|
| 0.472938 | 62.41285 |
| 0.384566 | 44.390142 |
| 0.179412 | 49.895425 |
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
- Started
- 2026-09-21 02:13:38.003816
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/96b7753d8e314763a196bb620423f685
| 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)