Advanced Topics

This section covers customization options beyond the basic fit() call. These features are useful for improving fitting accuracy through data normalization, incorporating prior knowledge about the Bézier simplex, or scaling your training to specialized hardware.

• Data Normalization
• Scikit-learn Integration
• Initial Control Points
• Bézier Simplex and Simploid Visualization
• Bézier Simplex Splitting
• Automatic Degree Selection
• Smoothness Regularization
• Elastic Net Grid Sampling
• Partial Training
• Advanced Sampling
• Hardware Acceleration
• Active Learning
• Simploidal Sets
• The Category of Simploidal Sets
• Pareto Set Learning by Stochastic Gradient Descent