README.md

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license: mit
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# Model card - Ikimina Reliability Index

**Artifacts:** Use the **Files and Versions** tab on this repository. 

**What this is:** CPU **XGBoost** (`tree_method="hist"`) + **`CalibratedClassifierCV`** (sigmoid, `cv=4`), saved as **`calibrated_model.joblib`** (scikit-learn / **joblib** — not Transformers). Load in Python with **`joblib.load`**. Use **`model_meta.json`** with it: **14** feature names in order (`feature_columns`), score formula, holdout **metrics**, tier labels.

**Output:** Reliability index in **[0, 100]** from default probability on label **`defaulted_within_6m`** (synthetic, 6-month horizon). Tiers: **0–40** high risk, **41–70** watch, **71–100** low risk.

**Data & split:** **Synthetic** data (`generate_data.py`, seed **42**), 500 members × 12 months. **Holdout:** last **100** ascending **`member_id`**. Inputs match **`ikimina_members.csv`** + group fields (`group_id`, `size`, `avg_contrib_xaf`, `urban_flag`, `founded_year`).

**Other files (optional):** `month_imputes.json`, `group_reliability.json` — short-history imputation and train-only group blend if your app uses them; core scoring uses the joblib + meta.

**Runtime:** CPython **3.10–3.12** recommended; needs **xgboost**, **scikit-learn**, **joblib**, **numpy**, **pandas**.

**Use / limits:** Prioritization / exploration only — **not** an automatic lending decision. Simulated labels; **no** fairness audit on protected attributes (not in schema). Hyperparameters **hand-tuned**, not full HPO.

**Privacy:** Scores only with **member consent** and **minimum necessary** identifiers.