The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the Scikit-Learn API paper:
- Consistency: All objects share a common interface drawn from a limited set of methods, with consistent documentation.
- Inspection: All specified parameter values are exposed as public attributes.
- Limited object hierarchy: Only algorithms are represented by Python classes; datasets are represented in standard formats (NumPy arrays, Pandas
DataFrames, SciPy sparse matrices) and parameter names use standard Python strings. - Composition: Many machine learning tasks can be expressed as sequences of more fundamental algorithms, and Scikit-Learn makes use of this wherever possible.
- Sensible defaults: When models require user-specified parameters, the library defines an appropriate default value.
