đ¤ Machine Learning
CSharpNumerics includes a lightweight, fully numerical machine learning framework designed for research, experimentation, and educational use. The focus is on transparency, mathematical clarity, and pipeline-based model evaluation â not black-box automation.
namespace CSharpNumerics.ML
| Module | Description |
|---|---|
| âī¸ Supervised AutoML | Automated pipeline search and model selection |
| đCross-Validation | K-fold, stratified, and custom validation strategies |
| đˇī¸Classification | Decision trees, logistic regression, and more |
| đ Regression | Linear, polynomial, and advanced regression models |
| đ Unsupervised AutoML | Automated pipeline, fluent API, clustering experiment |
| đĢ§ Clustering | Clustering models and evaluators |
| đ˛ Uncertainty Estimation | Monte Carlo bootstrap, consensus matrix, and stability analysis |
| đī¸Dimensionality Reduction | PCA and unsupervised preprocessing for supervised and clustering pipelines |
| âReinforcement AutoML | RL experiment runner, grid search, Monte Carlo evaluation |
| đšī¸RL Algorithms | Agents, policies, environments, replay buffers, and diagnostics |