Random Forest Result #146 — Analysis Report
Contents
- random forest (#146) completed
1. Random Forest
2026-09-21 02:15:02 · random_forest
The model explains 77.2% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.77179 |
| r2_std | 0.041005 |
| mse_mean | 199.71057 |
| mse_std | 27.668687 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.000762 | 0.008948 |
| ENV_SOILNH4 | -0.00042 | 0.002022 |
| ENV_SOILNO3 | -0.000454 | 0.004077 |
| ENV_SOILP | 0.004555 | 0.00288 |
| ENV_SOILOC | 0.002502 | 0.004561 |
| ENV_SOILTN | 0.016942 | 0.011589 |
| ENV_SOILTC | 0.018122 | 0.00958 |
| ENV_PH | -0.000352 | 0.000364 |
| ENV_SOILCLAY | -0.000108 | 0.000876 |
| ENV_SOILSILT | 0.005832 | 0.001824 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.47108 |
| ENV_SOILNH4 | 0.215687 |
| ENV_SOILNO3 | 0.136184 |
| ENV_SOILP | 0.350946 |
| ENV_SOILOC | 0.854207 |
| ENV_SOILTN | 0.358174 |
| ENV_SOILTC | 0.571677 |
| ENV_PH | 0.214236 |
| ENV_SOILCLAY | 0.299414 |
| ENV_SOILSILT | 0.290186 |
split metrics
| r2 | mse |
|---|---|
| 0.745856 | 234.197297 |
| 0.739838 | 198.477631 |
| 0.829675 | 166.456784 |
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_05_grasshopper_pest_pressure.csv
- Started
- 2026-09-21 02:15:02.446843
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/41da97babc1945769dd1bb43f3a17845
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | GH_COUNT |
| 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)