scikit-learn
machine-learning library
Classic ML library for Python
Supported languages
Concepts
estimatorstransformerspipelinescross-validation
Pros and Cons
Ventajas
- + Consistent and simple API
- + Excellent documentation
- + Very well-tested and stable
- + Integration with numpy/pandas
- + Consistent and easy-to-use API
- + Wide collection of classical algorithms
- + Excellent for learning ML
- + Perfect integration with NumPy/Pandas
- + Exemplary documentation
Desventajas
- - Not for deep learning
- - No native GPU support
- - Not for very large datasets
- - No deep learning support
- - Limited for big data
- - Not for neural networks
Casos de Uso
- Classic ML
- Data preprocessing
- Feature engineering
- Prototyping
- Classification and regression
- Clustering and dimensionality reduction
- Model selection and validation
- Traditional ML pipelines