# Ml Result Result #101 — Analysis Report

Generated 2026-09-21 02:36:22 · 3 run(s) · CAT-G Data Analyzer

## 1. pipeline stage — preprocessing (#101)

2026-09-21 02:12:52 · ml_result · status: completed

*Pipeline stage preprocessing: missing values: mean.*

### preprocessing

| Field | Value |
|---|---|
| stage | preprocessing |
| detail | missing values: mean |

### Method & Provenance

- **Method:** Random Forest
- **Engine:** Python · scikit-learn 1.8.0
- **Input datasets:** gallery — cat_g_field_data_plots.csv
- **Started:** 2026-09-21 02:12:52.641122
- **Permanent link:** https://cat-g-omxpert.wishartlab.com/results/by-token/a316fb7a42834890a899475de981b6f9

| Parameter | Value |
|---|---|
| model_type | random_forest |
| target_column | PC_SHOOTMASS |
| n_splits | 3 |

**Command history**

```python
# Models compared via app.services.ml_model_registry
```

```python
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)
```

## 2. pipeline stage — feature selection (#101)

2026-09-21 02:12:52 · ml_result · status: completed

*Pipeline stage feature selection: kbest_f (k=5); 40 → 5 features.*

### feature selection

| Field | Value |
|---|---|
| stage | feature_selection |
| detail | kbest_f (k=5); 40 → 5 features |

### Method & Provenance

- **Method:** Random Forest
- **Engine:** Python · scikit-learn 1.8.0
- **Input datasets:** gallery — cat_g_field_data_plots.csv
- **Started:** 2026-09-21 02:12:52.641122
- **Permanent link:** https://cat-g-omxpert.wishartlab.com/results/by-token/a316fb7a42834890a899475de981b6f9

| Parameter | Value |
|---|---|
| model_type | random_forest |
| target_column | PC_SHOOTMASS |
| n_splits | 3 |

**Command history**

```python
# Models compared via app.services.ml_model_registry
```

```python
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)
```

## 3. random forest (#101)

2026-09-21 02:12:52 · ml_result · status: completed

*The model explains 27.2% of the variance in PC_SHOOTMASS under cross-validation.*

| Metric | Value |
|---|---|
| r2_mean | 0.271886 |
| r2_std | 0.016556 |
| mse_mean | 63.288757 |
| mse_std | 5.592898 |
| task_type | regression |

### pipeline stages

| stage | detail |
|---|---|
| preprocessing | missing values: mean |
| feature_selection | kbest_f (k=5); 40 → 5 features |
| model | random_forest (regression), 3-fold cross-validation |

### feature importance

| name | value |
|---|---|
| ENV_SOILOC | 0.177931 |
| ENV_SOILTN | 0.133153 |
| ENV_SOILTC | 0.128963 |
| ENV_SOILMOIS | 0.154279 |
| RC_SHOOTMASS | 0.405673 |

### split metrics

| r2 | mse |
|---|---|
| 0.294885 | 65.113089 |
| 0.264187 | 69.041771 |
| 0.256587 | 55.711411 |

![importance bar](data:image/png;base64,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)

*importance bar*

![cv 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)

*cv boxplot*

### Method & Provenance

- **Method:** Random Forest
- **Engine:** Python · scikit-learn 1.8.0
- **Input datasets:** gallery — cat_g_field_data_plots.csv
- **Started:** 2026-09-21 02:12:52.641122
- **Permanent link:** https://cat-g-omxpert.wishartlab.com/results/by-token/a316fb7a42834890a899475de981b6f9

| Parameter | Value |
|---|---|
| model_type | random_forest |
| target_column | PC_SHOOTMASS |
| n_splits | 3 |

**Command history**

```python
# Models compared via app.services.ml_model_registry
```

```python
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)
```