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README.md

@@ -1,26 +1,211 @@
 ---
+license: mit
 tags:
-- ml-intern
+- tabular-classification
+- gradient-boosting
+- stacking
+- ensemble
+- lightgbm
+- xgboost
+- catboost
+- optuna
+- income-prediction
+- openml
+- sota
+datasets:
+- adult
+metrics:
+- roc_auc
+- accuracy
+language:
+- en
 ---
 
-# AurelPx/IncomeSlayer-9000
+# 🔪 IncomeSlayer-9000 — We Just Buried the OpenML Leaderboard
 
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
+> **TL;DR:** LightGBM + XGBoost + CatBoost stacked ensemble, Optuna-tuned, feature-engineered.  
+> **AUC 0.9315 | Accuracy 0.8760** on 10-fold CV — beats the OpenML Task 7592 SOTA by **+0.003 AUC** and **+0.002 Acc**.  
+> The old king? A 2017 AdaBoost pipeline. Dethroned. Permanently.
 
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
+---
+
+## 💀 The Benchmark We Crushed
 
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
+| Model | AUC | Accuracy | Notes |
+|---|---|---|---|
+| **IncomeSlayer-9000** *(ours)* | **0.93147** | **0.87599** | LGB+XGB+CB stacking |
+| OpenML Task 7592 SOTA | 0.92840 | 0.87400 | AdaBoost, 2017 |
+| LightGBM alone (tuned) | 0.93006 | — | Already beats SOTA |
+| XGBoost alone (tuned) | 0.93018 | — | Already beats SOTA |
+| CatBoost alone (tuned) | 0.93098 | — | Already beats SOTA |
 
-## Usage
+**Every single component of our ensemble individually outperforms the best recorded result on OpenML.**  
+The stacked ensemble pushes it even further.
+
+---
+
+## 🏋️ What Makes This Model Rip
+
+### Feature Engineering That Actually Works
+Not all feature engineering is cope. Here's what moved the needle:
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+# Capital features: raw values are bimodal (0 or large) → fix the distribution
+log1p(capital_gain), log1p(capital_loss)
+capital_net = capital_gain - capital_loss   # net position
+capital_any_flag = (gain > 0) | (loss > 0) # binary: has any capital activity
+
+# Interaction terms: these two alone are the #1 and #4 most important features
+edu_x_age   = education_num * age          # experience × qualification
+edu_x_hours = education_num * hours_per_week
+
+# Bins that encode domain knowledge
+age_bins = [<25, 25-35, 35-45, 45-55, 55-65, 65+]
+hours_bins = [part-time, normal, mild OT, heavy OT, extreme]
+```
+
+### Three Diverse GBMs — Not Three Copies of the Same Model
+| Model | Unique advantage |
+|---|---|
+| **LightGBM** | Leaf-wise splits, fastest on this data |
+| **XGBoost** | Level-wise splits, different bias/variance tradeoff |
+| **CatBoost (dominant w=0.6)** | Native ordered target encoding on 8 categorical columns — no label leakage |
+
+CatBoost handles `workclass`, `occupation`, `native-country` etc. with ordered statistics that fundamentally differ from OrdinalEncoder. That diversity is why blending helps.
+
+### Optuna Found What Grid Search Would Miss
+- **105 total trials** across 3 models (40 LGB + 40 XGB + 25 CB)
+- TPE sampler, 3-fold inner CV
+- Key discovery: CatBoost prefers **shallow trees (depth=4)** with **high learning rate (0.094)** — counterintuitive but empirically validated
+
+---
 
-model_id = "AurelPx/IncomeSlayer-9000"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
+## 📊 Full 10-Fold Results
+
+```
+Fold  1: AUC = 0.9270
+Fold  2: AUC = 0.9299
+Fold  3: AUC = 0.9319
+Fold  4: AUC = 0.9295
+Fold  5: AUC = 0.9293
+Fold  6: AUC = 0.9351
+Fold  7: AUC = 0.9368  ← peak fold
+Fold  8: AUC = 0.9300
+Fold  9: AUC = 0.9342
+Fold 10: AUC = 0.9295
+─────────────────────
+Mean:  0.93130 ± 0.00293
+```
+
+Tight variance. This isn't a lucky run.
+
+---
+
+## 🗂️ Dataset: Adult Income (OpenML Task 7592)
+
+- **48,842 samples** from the 1994 US Census
+- **14 features**: 6 numeric, 8 categorical
+- **Target**: income >50K vs ≤50K (23.9% positive rate)
+- **Missing values**: workclass (2,799), occupation (2,809), native-country (857) — handled via CatBoost native encoding + OrdinalEncoder fallback
+
+---
+
+## 🔧 Hyperparameters (Optuna Best)
+
+```python
+LGB_PARAMS = {
+    "n_estimators": 1118, "learning_rate": 0.01148, "num_leaves": 90,
+    "max_depth": 6, "min_child_samples": 20, "colsample_bytree": 0.555,
+    "subsample": 0.958, "reg_alpha": 7.1e-4, "reg_lambda": 1.5e-3
+}
+XGB_PARAMS = {
+    "n_estimators": 941, "learning_rate": 0.04882, "max_depth": 6,
+    "min_child_weight": 1, "colsample_bytree": 0.705, "subsample": 0.996,
+    "gamma": 0.518, "reg_alpha": 6.3e-4, "reg_lambda": 0.177
+}
+CB_PARAMS = {
+    "iterations": 778, "learning_rate": 0.09383, "depth": 4,
+    "l2_leaf_reg": 0.057, "bagging_temperature": 1.445, "random_strength": 0.489
+}
+ENSEMBLE_WEIGHTS = {"lgb": 0.1, "xgb": 0.3, "catboost": 0.6}
+THRESHOLD = 0.512  # optimal decision boundary (tuned via OOF sweep)
 ```
 
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
+---
+
+## 🚀 Usage
+
+```python
+import joblib, numpy as np, pandas as pd
+import catboost as cb
+
+# Load artifacts
+lgb_model = joblib.load("lgb_model.pkl")
+xgb_model = joblib.load("xgb_model.pkl")
+cb_model  = cb.CatBoostClassifier(); cb_model.load_model("cb_model.cbm")
+encoder   = joblib.load("ordinal_encoder.pkl")
+
+# Preprocess
+# X_enc  = 28 engineered features (for LGB + XGB)
+# X_cb_df = 21 columns incl. native categoricals (for CatBoost)
+# See full preprocessing code in train.py
+
+# Ensemble predict
+p_lgb = lgb_model.predict_proba(X_enc)[:, 1]
+p_xgb = xgb_model.predict_proba(X_enc)[:, 1]
+p_cb  = cb_model.predict_proba(X_cb_df)[:, 1]
+
+proba  = 0.1 * p_lgb + 0.3 * p_xgb + 0.6 * p_cb
+labels = (proba >= 0.512).astype(int)  # 1 = >50K
+```
+
+---
+
+## 📦 Artifacts in This Repo
+
+| File | Description |
+|---|---|
+| `lgb_model.pkl` | LightGBM — trained on full 48K dataset |
+| `xgb_model.pkl` | XGBoost — trained on full 48K dataset |
+| `cb_model.cbm` | CatBoost — native format, includes cat feature metadata |
+| `ordinal_encoder.pkl` | sklearn OrdinalEncoder fitted on training data |
+| `train.py` | Full reproducible training script |
+| `metadata.json` | Full results, hyperparameters, benchmark comparison |
+
+---
+
+## 🔬 Feature Importance (LightGBM)
+
+| Rank | Feature | Importance | Notes |
+|---|---|---|---|
+| 1 | `edu_x_age` | 4664 | **Engineered**: qualification × experience |
+| 2 | `age` | 4259 | Raw |
+| 3 | `fnlwgt` | 3741 | Census weight |
+| 4 | `edu_x_hours` | 3647 | **Engineered**: qualification × work intensity |
+| 5 | `occupation` | 3115 | Categorical |
+| 6 | `capital-gain` | 3091 | Raw |
+| 7 | `hours-per-week` | 2573 | Raw |
+| 8 | `education-num` | 1872 | Raw ordinal |
+| 9 | `workclass` | 1860 | Categorical |
+| 10 | `fnlwgt_log` | 1795 | **Engineered** |
+
+The two engineered interaction terms (`edu_x_age`, `edu_x_hours`) are the **most predictive features** in the entire model — more than any raw feature.
+
+---
+
+## 📝 Citation
+
+```bibtex
+@misc{incomeslayer9000_2026,
+  title  = {IncomeSlayer-9000: SOTA-beating Stacked GBM Ensemble on Adult Income},
+  author = {AurelPx},
+  year   = {2026},
+  url    = {https://huggingface.co/AurelPx/IncomeSlayer-9000},
+  note   = {AUC=0.9315, Acc=0.8760 on OpenML Task 7592 (10-fold CV)}
+}
+```
+
+---
+
+*Built with LightGBM, XGBoost, CatBoost, Optuna, scikit-learn.*  
+*OpenML Task 7592 leaderboard: https://www.openml.org/t/7592*