Random Forest Result #140 — Analysis Report
Contents
- random forest (#140) completed
1. Random Forest
2026-09-21 02:12:05 · random_forest
The classifier predicts the target with 100.0% cross-validated accuracy, weighted F1 1.000.
| task_type | classification |
|---|---|
| accuracy_mean | 1.0 |
| accuracy_std | 0.0 |
| f1_mean | 1.0 |
| f1_std | 0.0 |
| n_classes | 3 |
permutation importance
| name | mean | std |
|---|---|---|
| PLOT | 0.0 | 0.0 |
| LAT | 0.004667 | 0.0066 |
| LONG | 0.011 | 0.004967 |
| ENV_SOILNH4 | 0.0 | 0.0 |
| ENV_SOILNO3 | 0.0 | 0.0 |
| ENV_SOILP | 0.0 | 0.0 |
| ENV_SOILOC | 0.004667 | 0.0066 |
| ENV_SOILTN | 0.0 | 0.0 |
| ENV_SOILTC | 0.0 | 0.0 |
| ENV_PH | 0.0 | 0.0 |
shap importance
| name | mean_abs |
|---|---|
| PLOT | 0.066248 |
| LAT | 0.02633 |
| LONG | 0.077888 |
| ENV_SOILNH4 | 0.0 |
| ENV_SOILNO3 | 0.000331 |
| ENV_SOILP | 0.0 |
| ENV_SOILOC | 0.013416 |
| ENV_SOILTN | 0.01395 |
| ENV_SOILTC | 0.013066 |
| ENV_PH | 0.0 |
split metrics
| accuracy | f1_weighted |
|---|---|
| 1.0 | 1.0 |
| 1.0 | 1.0 |
| 1.0 | 1.0 |
importance bar
permutation bar
shap summary
confusion heatmap
cv boxplot
Method & Provenance
- Method
- Random Forest (Classification)
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — cat_g_field_data_plots.csv
- Started
- 2026-09-21 02:12:05.989988
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/76d644e54c38460abc9ca6f21474ff98
| Parameter | Value |
|---|---|
| task_type | classification |
| model_type | classification |
| target_column | RANCH |
| 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 RandomForestClassifier # python model = RandomForestClassifier(n_estimators=60, random_state=42) # python scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)