Random Forest Result #145 — Analysis Report
Contents
- random forest (#145) completed
1. Random Forest
2026-09-21 02:14:42 · random_forest
The model explains 78.4% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.784445 |
| r2_std | 0.027108 |
| mse_mean | 0.028559 |
| mse_std | 0.001398 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.001728 | 0.001922 |
| ENV_SOILNH4 | 0.001038 | 0.001454 |
| ENV_SOILNO3 | -0.000827 | 0.001448 |
| ENV_SOILP | -0.001439 | 0.001833 |
| ENV_SOILOC | 0.002019 | 0.000753 |
| ENV_SOILTN | -0.000309 | 0.000756 |
| ENV_SOILTC | -0.000263 | 0.001078 |
| ENV_PH | 0.001013 | 0.000999 |
| ENV_SOILCLAY | -0.001452 | 0.00374 |
| ENV_SOILSILT | 1.2e-05 | 0.001276 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.002544 |
| ENV_SOILNH4 | 0.001869 |
| ENV_SOILNO3 | 0.002629 |
| ENV_SOILP | 0.002738 |
| ENV_SOILOC | 0.002754 |
| ENV_SOILTN | 0.001206 |
| ENV_SOILTC | 0.001763 |
| ENV_PH | 0.006021 |
| ENV_SOILCLAY | 0.002991 |
| ENV_SOILSILT | 0.002607 |
split metrics
| r2 | mse |
|---|---|
| 0.746124 | 0.030228 |
| 0.80457 | 0.026807 |
| 0.802639 | 0.028641 |
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_04_vegetation_diversity.csv
- Started
- 2026-09-21 02:14:42.722532
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/023c0e89aaf94a488763f77c20de38a3
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | COVER_SHANNON_JUL |
| 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)