Welcome to MAterials Simulation Toolkit for Machine Learning (MAST-ML)’s documentation!¶
- Acknowledgements
- Installing MAST-ML
- MAST-ML Input File
- MAST-ML overview slides
- Running MAST-ML on Google Colab
- MAST-ML tutorial
- Introduction
- Your first MAST-ML run
- Cleaning input data
- Feature generation and normalization
- Training and evaluating your first model
- Feature selection and learning curves
- Hyperparameter optimization
- Random leave-out versus leave-out-group cross-validation
- Making predictions by importing a previously fit model
- Predicting values for new, extrapolated data
- Code Documentation: Metrics
- Code Documentation: Configuration file parser
- Code Documentation: Data cleaner
- Code Documentation: Data loader
- Code Documentation: Learning curve
- Code Documentation: Clusterers
- Code Documentation: Data splitters
- Code Documentation: Utils
- Code Documentation: MAST-ML Driver
- Code Documentation: Plot Helper
- Code Documentation: HTML Helper
- Code Documentation: Feature Selectors
- Code Documentation: Feature Normalizers
- Code Documentation: Randomizers
- Code Documentation: Model Finder
- Code Documentation: Utility Legos
- Code Documentation: Feature Generators