Random Forest Result #149 — Analysis Report
Contents
- random forest (#149) completed
1. Random Forest
2026-09-21 02:16:12 · random_forest
The model explains 0.0% of the variance in the target — worse than simply predicting the mean; treat predictions as unreliable.
| task_type | regression |
|---|---|
| r2_mean | -0.127684 |
| r2_std | 0.430563 |
| mse_mean | 3.420748 |
| mse_std | 0.823789 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.016337 | 0.003928 |
| ENV_SOILNH4 | 0.004143 | 0.002695 |
| ENV_SOILNO3 | 0.007993 | 0.006056 |
| ENV_SOILP | 0.002755 | 0.00336 |
| ENV_SOILOC | -0.005316 | 0.003437 |
| ENV_SOILTN | -0.000658 | 0.003885 |
| ENV_SOILTC | -0.005923 | 0.008819 |
| ENV_PH | 0.00459 | 0.008166 |
| ENV_SOILCLAY | 0.028921 | 0.009043 |
| ENV_SOILSILT | -0.003407 | 0.006026 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.070773 |
| ENV_SOILNH4 | 0.019476 |
| ENV_SOILNO3 | 0.028227 |
| ENV_SOILP | 0.020736 |
| ENV_SOILOC | 0.004994 |
| ENV_SOILTN | 0.003569 |
| ENV_SOILTC | 0.005338 |
| ENV_PH | 0.026288 |
| ENV_SOILCLAY | 0.100922 |
| ENV_SOILSILT | 0.044507 |
split metrics
| r2 | mse |
|---|---|
| -0.732183 | 3.698357 |
| 0.111214 | 4.261812 |
| 0.237916 | 2.302074 |
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_08_woody_encroachment.csv
- Started
- 2026-09-21 02:16:12.854253
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/c2d3ecce80614f57be4dc0be42e7fcdb
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | PC_WOODMASS |
| 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)