---
library_name: sklearn
tags:
- anomaly-detection
- isolation-forest
- logistics
- quality
- risk-scoring
pipeline_tag: tabular-classification
license: apache-2.0
---

# Isolation Forest — Daily Transport Quality Risk Model

> **Model:** `isolation_forest_quality.joblib`
> **Task:** unsupervised anomaly detection on daily shipment-quality records
> **Training date:** 2026-08-13
> **Dataset:** `toolathon123/project_20260813_014231_9dbe9212` (2,000 shipments)

Pure **NumPy implementation of an Isolation Forest** (Liu, Ting & Zhou, 2008). The algorithm isolates
observations by randomly partitioning the feature space with binary trees; points that are *easy to isolate*
(i.e. require few splits) are anomalous. Because scikit-learn is not installed in the pipeline runtime, the
model is shipped as a self-contained `joblib` blob with no external runtime dependency beyond NumPy.

## Loading the model (reproducible)

```python
import sys, joblib
from huggingface_hub import hf_hub_download

# 1) put the implementation module on the path
sys.path.insert(0, "model")           # model/isolation_forest.py
# (or: pip-style: download model/isolation_forest.py and import it)

# 2) download and load the serialized model
path = hf_hub_download("toolathon123/project_20260813_014231_9dbe9212",
                       "model/isolation_forest_quality.joblib", repo_type="dataset")
model = joblib.load(path)             # -> isolation_forest.IsolationForest
```

`model/isolation_forest.py` is the importable source of the model class — required so the pickle can
resolve the `IsolationForest` class on any machine.

## Model inputs (features)

The model consumes the 6 numeric features of each shipment:

| # | Feature | Description |
|---|---------|-------------|
| 1 | `delay_minutes` | Arrival delay in minutes |
| 2 | `damage_flag` | 1 if cargo damaged, else 0 |
| 3 | `temperature_deviation` | Deviation from target temperature (°C) |
| 4 | `humidity_deviation` | Deviation from target relative humidity (%RH) |
| 5 | `fuel_consumption` | Total fuel consumed (litres) |
| 6 | `quality_score` | Composite quality score 0–100 |

Categorical fields (`route`, `carrier`, `origin`, `destination`) are not used for scoring; they are kept in
the dataset for drill-down and reporting.

## Outputs

- **`anomaly_score`** ∈ [0.5, 1.0] — Isolation Forest anomaly score. Higher ⇒ more anomalous.
- **`high_risk`** ∈ {0, 1} — flag = 1 when `anomaly_score >= threshold` (0.6044), i.e.
  the shipment is flagged for manual review as a **high-quality-risk order**.

## Daily update strategy

- The model is **retrained every operational day** on that day's fresh `train` snapshot
  (`data/train-*.parquet`).
- Hyper-parameters: `n_estimators=100`, `max_samples=256`, `contamination=0.05` (anomaly threshold = 95th
  percentile of training anomaly scores).
- `model_metadata.json` stores the trained thresholds, feature list and the day's KPIs for full traceability
  and comparison across days.

## Performance / daily KPIs (2026-08-13)

| Metric | Value |
|--------|-------|
| High-risk order ratio | 5.00% (100 / 2000) |
| Avg delay (all shipments) | 83.2 min |
| Avg delay (low-risk) | 77.4 min |
| Avg delay (high-risk) | 192.7 min |
| **Avg delay improvement rate** | **6.93%** |
| Damage rate | 8.10% |
| Avg quality score | 85.52 |
| Avg temperature deviation | 0.63 °C |
| Avg humidity deviation | 4.57 %RH |

*Delay improvement rate = relative reduction in mean delay if the 100 high-risk shipments
were remediated to the low-risk mean delay.*

## Intended use

Operational daily triage: prioritise manual inspection and re-planning for the top high-risk shipments,
targeting the QIP goal of pushing the high-risk ratio below 5%.

## Files

- `isolation_forest_quality.joblib` — serialized model (joblib)
- `isolation_forest.py` — importable NumPy IsolationForest implementation
- `model_metadata.json` — training metadata, features, threshold and daily KPIs