Random Forest Result #148 — Analysis Report
Contents
- random forest (#148) completed
1. Random Forest
2026-09-21 02:15:49 · random_forest
The model explains 93.6% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.935626 |
| r2_std | 0.055255 |
| mse_mean | 32.913705 |
| mse_std | 29.804256 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.000829 | 0.001282 |
| ENV_SOILNH4 | -0.003147 | 0.004054 |
| ENV_SOILNO3 | 8.9e-05 | 0.000354 |
| ENV_SOILP | -0.000501 | 0.000819 |
| ENV_SOILOC | 2.4e-05 | 0.000269 |
| ENV_SOILTN | 0.00232 | 0.002203 |
| ENV_SOILTC | 0.000416 | 0.000181 |
| ENV_PH | -0.000126 | 0.000154 |
| ENV_SOILCLAY | 0.001992 | 0.000425 |
| ENV_SOILSILT | 0.000718 | 0.001379 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.195952 |
| ENV_SOILNH4 | 0.10979 |
| ENV_SOILNO3 | 0.064227 |
| ENV_SOILP | 0.100928 |
| ENV_SOILOC | 0.017297 |
| ENV_SOILTN | 0.077718 |
| ENV_SOILTC | 0.185985 |
| ENV_PH | 0.02226 |
| ENV_SOILCLAY | 0.558899 |
| ENV_SOILSILT | 0.415885 |
split metrics
| r2 | mse |
|---|---|
| 0.990608 | 3.079392 |
| 0.860047 | 73.616049 |
| 0.956222 | 22.045674 |
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_07_microbial_soil_biology.csv
- Started
- 2026-09-21 02:15:49.155216
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/8705c62fa577496199d0d6e6fc4f47a1
| Parameter | Value |
|---|---|
| task_type | regression |
| model_type | regression |
| target_column | MICROBIAL_NECROMASS_C |
| 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)