LionBook Chapter 5: Mastering generalized linear least-squares
After reading this chapter you are expected to improve from a casual modeler to a professional least-squares guru. Losing accuracy is not a weakness but a strength, an opportunity to create more powerful models by simplifying the analysis.
Here is the latest chapter from LIONbook, a new book dedicated to “LION” combination of Machine Learning and Intelligent Optimization, written by the developers of LionSolver software, Roberto Battiti and Mauro Brunato.
This book will available for free from the web, chapter after chapter.
Here are previous chapters:
- Chapters 1-2: Introduction and nearest neighbors.
- Chapter 3: Learning requires a method
- Chapter 4: Linear models
and the latest,
Chapter 5: Mastering generalized linear least-squares
… Usually one is dealing with multiple modeling architectures, withjudging the quality of a model (the goodness-of-fit in our case) andselecting the best possible architecture, with determining confidence regions (e.g., error bars) for the estimated model parameters,etc. After reading this chapter you are supposed to raise from thestatus of casual user to
that of professional least-squares guru.