| Name | sbc-model JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Un clasificador de Machine Learning que implementa un ensamblaje de Stacking con modelos de Boosting. |
| upload_time | 2025-09-15 17:06:47 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Pablo Eduardo Chavez Mercado
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
stacking
boosting
classifier
ensemble
xgboost
machine-learning
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# sbc-model 🤖
[](https://badge.fury.io/py/sbc-model)
[](https://opensource.org/licenses/MIT)
Un clasificador de Machine Learning fácil de usar que implementa un ensamblaje de Stacking con modelos de Boosting (XGBoost, LightGBM, CatBoost).
## ¿Qué es sbc-model? 🤔
`sbc-model` es una librerÃa de alto nivel diseñada para simplificar el proceso de creación de modelos de ensamblaje robustos. En lugar de configurar manualmente la validación cruzada y el meta-modelo, `sbc-model` lo encapsula en una sola clase, siguiendo las mejores prácticas de scikit-learn.
El nombre **SBC** significa **S**tacking **B**oosting **C**lassifier.
---
## CaracterÃsticas Principales ✨
* **Modelos Potentes:** Utiliza XGBoost, LightGBM y CatBoost como modelos base, tres de los algoritmos más potentes para datos tabulares.
* **Stacking Automatizado:** Gestiona automáticamente el proceso de validación cruzada para generar predicciones "out-of-fold" y entrenar un meta-modelo.
* **Fácil de Usar:** Interfaz simple inspirada en scikit-learn. Solo necesitas instanciar la clase y llamar a `.fit_predict_proba()`.
* **Reproducible:** Controla la aleatoriedad con una `seed` para asegurar que tus resultados sean consistentes.
---
## Instalación 📦
Puedes instalar `sbc-model` directamente desde PyPI:
```bash
pip install sbc-model
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"description": "# sbc-model \ud83e\udd16\r\n\r\n[](https://badge.fury.io/py/sbc-model)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\nUn clasificador de Machine Learning f\u00e1cil de usar que implementa un ensamblaje de Stacking con modelos de Boosting (XGBoost, LightGBM, CatBoost).\r\n\r\n## \u00bfQu\u00e9 es sbc-model? \ud83e\udd14\r\n\r\n`sbc-model` es una librer\u00eda de alto nivel dise\u00f1ada para simplificar el proceso de creaci\u00f3n de modelos de ensamblaje robustos. En lugar de configurar manualmente la validaci\u00f3n cruzada y el meta-modelo, `sbc-model` lo encapsula en una sola clase, siguiendo las mejores pr\u00e1cticas de scikit-learn.\r\n\r\nEl nombre **SBC** significa **S**tacking **B**oosting **C**lassifier.\r\n\r\n---\r\n\r\n## Caracter\u00edsticas Principales \u2728\r\n\r\n* **Modelos Potentes:** Utiliza XGBoost, LightGBM y CatBoost como modelos base, tres de los algoritmos m\u00e1s potentes para datos tabulares.\r\n* **Stacking Automatizado:** Gestiona autom\u00e1ticamente el proceso de validaci\u00f3n cruzada para generar predicciones \"out-of-fold\" y entrenar un meta-modelo.\r\n* **F\u00e1cil de Usar:** Interfaz simple inspirada en scikit-learn. Solo necesitas instanciar la clase y llamar a `.fit_predict_proba()`.\r\n* **Reproducible:** Controla la aleatoriedad con una `seed` para asegurar que tus resultados sean consistentes.\r\n\r\n---\r\n\r\n## Instalaci\u00f3n \ud83d\udce6\r\n\r\nPuedes instalar `sbc-model` directamente desde PyPI:\r\n\r\n```bash\r\npip install sbc-model\r\n",
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