Tabular Regression
Scikit-learn
Joblib
sklearn-pipeline
card-game
flesh-and-blood
game-balance
educational
Instructions to use 4math/FAB_Prediction_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use 4math/FAB_Prediction_Model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("4math/FAB_Prediction_Model", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "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": "2026-03-31T11:29:18.400928", | |
| "metrics": { | |
| "train_r2": 0.917, | |
| "train_mse": 0.0049, | |
| "train_mae": 0.042, | |
| "cv_r2_mean": -1.3332, | |
| "cv_r2_std": 2.1072, | |
| "n_samples": 9, | |
| "n_features": 17, | |
| "training_mode": "local" | |
| }, | |
| "educational": true, | |
| "disclaimer": "Fan-made project. NOT affiliated with Legend Story Studios." | |
| } |