Random Forest Result #138 — Analysis Report
Contents
- random forest (#138) completed
1. Random Forest
2026-09-21 02:11:08 · random_forest
The model explains 54.4% of the variance in the target under cross-validation.
| task_type | regression |
|---|---|
| r2_mean | 0.544146 |
| r2_std | 0.01283 |
| mse_mean | 39.370091 |
| mse_std | 4.061467 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.000306 | 0.000724 |
| LAT | 0.007732 | 0.001774 |
| LONG | 0.001503 | 0.00154 |
| ENV_SOILNH4 | 0.003629 | 0.002743 |
| ENV_SOILNO3 | -0.000594 | 0.002811 |
| ENV_SOILP | 0.000928 | 0.00248 |
| ENV_SOILOC | 0.01288 | 0.008753 |
| ENV_SOILTN | 0.011657 | 0.002143 |
| ENV_SOILTC | 0.00416 | 0.007555 |
| ENV_PH | -0.002956 | 0.00333 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.086385 |
| LAT | 0.241393 |
| LONG | 0.050582 |
| ENV_SOILNH4 | 0.09909 |
| ENV_SOILNO3 | 0.068239 |
| ENV_SOILP | 0.055237 |
| ENV_SOILOC | 0.104043 |
| ENV_SOILTN | 0.720728 |
| ENV_SOILTC | 0.300837 |
| ENV_PH | 0.173977 |
split metrics
| r2 | mse |
|---|---|
| 0.559827 | 44.486553 |
| 0.528399 | 39.072311 |
| 0.544211 | 34.551409 |
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 — cat_g_field_data_plots.csv
- Started
- 2026-09-21 02:11:08.707331
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/e38fdc04752c42c5a01d7772f67245f3
| 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)