Metadata-Version: 2.4
Name: arvores-henrique-soares
Version: 1.0.0
Summary: Implementação dos algoritmos de árvore de decisão ID3, C4.5 e CART do zero
Author-email: Henrique Soares <henrique.soares@exemplo.com>
Maintainer-email: Henrique Soares <henrique.soares@exemplo.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/HenriqueSoares28/lista4_ia
Project-URL: Repository, https://github.com/HenriqueSoares28/lista4_ia
Project-URL: Documentation, https://github.com/HenriqueSoares28/lista4_ia#readme
Project-URL: Bug Reports, https://github.com/HenriqueSoares28/lista4_ia/issues
Keywords: machine-learning,decision-trees,id3,c45,cart,classification,artificial-intelligence,data-science
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.19.0
Requires-Dist: pandas>=1.1.0
Requires-Dist: scikit-learn>=0.24.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: pytest-cov>=2.0; extra == "dev"
Requires-Dist: black>=21.0; extra == "dev"
Requires-Dist: flake8>=3.8; extra == "dev"
Requires-Dist: mypy>=0.800; extra == "dev"
Provides-Extra: examples
Requires-Dist: matplotlib>=3.3.0; extra == "examples"
Requires-Dist: seaborn>=0.11.0; extra == "examples"
Requires-Dist: jupyter>=1.0.0; extra == "examples"
Dynamic: license-file

# Minhas Árvores

Implementação educacional dos algoritmos ID3, C4.5 e CART em Python.

## Instalação

```bash
pip install numpy pandas scikit-learn
```

## Uso

```python
from minhas_arvores import ID3, C45, CART
import pandas as pd

dados = pd.DataFrame({
    'Tempo': ['Sol', 'Nublado', 'Chuva'],
    'Jogar': ['Não', 'Sim', 'Sim']
})

X = dados[['Tempo']]
y = dados['Jogar']

# Treinar
id3 = ID3()
id3.fit(X, y)

# Predizer
resultado = id3.predict({'Tempo': 'Sol'})
print(resultado)
```

## Exemplo Completo

```bash
python exemplo_uso.py
```

## Algoritmos

- **ID3**: Ganho de informação, categóricos apenas
- **C4.5**: Razão de ganho, suporta contínuos e missing values  
- **CART**: Gini index, divisões binárias

## Licença

MIT

