metadata
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 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
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