Upload folder using huggingface_hub

README.md

@@ -1,3 +1,95 @@
 ---
 license: mit
+tags:
+  - tabular-regression
+  - sklearn
+  - card-game
+  - flesh-and-blood
+  - game-balance
+  - educational
+  - sagemaker
+library_name: sklearn
+pipeline_tag: tabular-regression
+datasets:
+  - custom
+metrics:
+  - r2
+  - mse
+  - mae
 ---
+
+# Teklovossen Card Balance Predictor
+
+> **Educational Project — NOT affiliated with Legend Story Studios or the official Flesh and Blood game.**
+
+A scikit-learn RandomForest regression model that predicts **win rates** for custom
+Teklovossen (Mechanologist) card decks in the [Flesh and Blood](https://fabtcg.com/) TCG.
+
+Trained on local using simulated gameplay data.
+
+## Model
+
+| Detail | Value |
+|--------|-------|
+| Type | `Pipeline(StandardScaler → RandomForestRegressor)` |
+| Task | Tabular regression — predict avg win rate |
+| Framework | scikit-learn 1.8.0 |
+| Training | local (local machine) |
+
+## Features (19 inputs)
+
+| # | Feature | Description |
+|---|---------|-------------|
+| 0 | AI | AI-themed card count |
+| 1 | Nano | Nano-themed card count |
+| 2 | Quantum | Quantum-themed card count |
+| 3 | Base | Base card count |
+| 4-8 | 0_cost … 4_plus_cost | Cost curve distribution |
+| 9-11 | equipment, item, action | Card type counts |
+| 12 | avg_turns | Average game length |
+| 13 | avg_evos | Average Evo equipment used |
+| 14 | avg_teklo_energy | Avg Teklo Energy generated |
+| 15 | avg_nanite_counters | Avg Nanite counters |
+| 16 | avg_quantum_charges | Avg Quantum charges |
+| 17 | consistency_score | Deck consistency (0-1) |
+| 18 | avg_expert_balance_score | Expert balance score (1-10) |
+
+## Output
+
+`avg_win_rate` — float [0, 1].
+
+## Metrics
+
+| Metric | Value |
+|--------|-------|
+| Train R² | 0.7666 |
+| CV R² (mean ± std) | 0.1381 ± 0.2349 |
+| Train MSE | 0.0775 |
+| Train MAE | 0.163 |
+| Samples | 9 |
+| Features | 17 |
+
+## Quick Start
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib, numpy as np
+
+path = hf_hub_download(repo_id="4math/FAB_Prediction_Model", filename="model.joblib")
+model = joblib.load(path)
+
+# Example: a Nano-focused deck
+features = np.array([[9,0,0,2, 5,2,1,0, 9,9,12, 0.6, 9.4,13.6,7.3, 0.74, 6.7, 0,0]])
+print(model.predict(features))  # → predicted win rate
+```
+
+## Limitations
+
+- Trained on **simulated** data, not real tournament results
+- Small dataset (9 samples) — rough estimates only
+- Simplified card mechanics in the simulation engine
+- Fan-made cards only
+
+## License
+
+MIT

config.json

@@ -0,0 +1,38 @@
+{
+  "model_type": "sklearn-pipeline",
+  "task": "tabular-regression",
+  "target": "avg_win_rate",
+  "features": [
+    "AI",
+    "Nano",
+    "Quantum",
+    "Base",
+    "0_cost",
+    "1_cost",
+    "2_cost",
+    "3_cost",
+    "4_plus_cost",
+    "equipment",
+    "item",
+    "action",
+    "avg_turns",
+    "avg_evos",
+    "avg_teklo_energy",
+    "avg_nanite_counters",
+    "avg_quantum_charges"
+  ],
+  "sklearn_version": "1.8.0",
+  "trained_at": "2026-03-31T11:14:11.174996",
+  "metrics": {
+    "train_r2": 0.7666,
+    "train_mse": 0.0775,
+    "train_mae": 0.163,
+    "cv_r2_mean": 0.1381,
+    "cv_r2_std": 0.2349,
+    "n_samples": 9,
+    "n_features": 17,
+    "training_mode": "local"
+  },
+  "educational": true,
+  "disclaimer": "Fan-made project. NOT affiliated with Legend Story Studios."
+}

model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56cacbb15ec89759267022bc2e609577274ed94b41c2bef967e09a54f08a0251
+size 13306