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This folder contains all the spreadsheets of my upcoming machine learning book. Currently, the following spreadshhets are available:

  • HDTdata4Excel.xlsx: hidden decision trees (ensemble method). See the related article on MLTechniques.com, here. A PDF version with detailed explanations, originally written in LaTeX and entitled Advanced Machine Learning with Basic Excel is available at MLTechniques.com/resources.
  • Shapes4.xlsx: related to shape signature, matching, comparison and classification. See the related article on MLTechniques.com, here. A PDF version with detailed explanations, originally written in LaTeX and entitled Classification of Shapes via Explainable AI is available at MLTechniques.com/resources.
  • Regression5.xlsx and Regression5_Static.xlsx: interpretable linear regression on synthetic data, respectively full spreadsheet and spreadsheet with regression coefficients and performance metrics for all the possible feature configurations. See article about it, here. The technical PDF version with detailed explanations, originally written in LaTeX and entitled Interpretable Machine Learning on Synthetic Data, and Little Known Secrets About Linear Regression is available at MLTechniques.com/resources.
  • Fuzzy4.xlsz is related to the fuzzy regression method. It is too large for GitHub (13 MB) and stored on Sync.com instead. The smaller spreadsheet, which corresponds to the small output file of the Python program fuzzy.py, is available here. The technical PDF version with detailed explanations, originally written in LaTeX and entitled Interpretable Machine Learning: Multipurpose, Model-free, Math-free Fuzzy Regression is available at MLTechniques.com/resources.
  • Linear2.xlsz is related to my gentle introduction to linear algebra and time series. It is too large for GitHub (28 MB) and stored on Sync.com instead. The smaller spreadsheet is available here. The technical PDF version with detailed explanations, originally written in LaTeX and entitled Gentle Introduction to Linear Algebra, with Spectacular Applications is available at MLTechniques.com/resources.