Fuzzy Regression: A Generic, Model-free, Math-free Machine Learning Technique

 A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical engineering, no regression coefficients, no bootstrap. Multivariate and highly non-linear. Interpretable and illustrated on synthetic data. Read more here.

For years, I have developed machine learning techniques that barely use any mathematics. I view it as a sport. Not that I don’t know anything about mathematics, quite the contrary. I believe you must be very math-savvy to achieve such accomplishments. This article epitomizes math-free machine learning. It is the result of years of research. The highly non-linear methodology described here may not be easier to grasp than math-heavy techniques. It has its own tricks. Yet, you could, in principle, teach it to middle school students.

Fuzzy regression with prediction intervals, original version, 1D

I did not in any way compromise on the quality and efficiency of the technique, for the sake of gaining the “math-free” label. What I describe here is a high performing technique in its own right. You can use it to solve various problems: multivariate regression, interpolation, data compression, prediction, or spatial modeling (well, without “model”). It comes with prediction intervals. Yet there is no statistical or probability model behind it, no calculus, no matrix algebra, no regression coefficients, no bootstrapping, no resampling, not even square roots.

Read the full article, and access the full technical report, Python code and data sets (all, free, no sign-up required), from here.

New Book: Approaching (Almost) Any Machine Learning Problem

This self-published book is dated July 2020 according to Amazon. But it appears to be an ongoing project. Like many new books, the material is on GitHub. The most recent version, dated June 2021, is available in PDF format.

This is not a traditional book. It feels like a repository of Python code, printed on paper if you buy the print version. The associated GitHub repository is much more useful if you want to re-use the code with simple copy and paste. It covers a lot of topics and performance metrics, with emphasis on computer vision problems. The code is documented in details. The code represents 80% of the content, and the comments in the code should be considered as an important, integral part of the content.

A Non-traditional Book

That said, the book is not an introduction to machine learning algorithms. It assumes some knowledge of the algorithms discussed, and there is no mathematical explanations. I find it to be an excellent 300-page Python tutorial covering many ML topics (maybe too many). The author focuses on real problems and real data. The style is very far from academic, and in my opinion, anti-academic.

Read the full review, see table of contents, and get the book, here.

How to Create/Use Great Synthetic Data for Interpretable Machine Learning

I share here my new article on synthetic data and interpretable machine learning. It will show you how to set up such an environment. I also mention three popular books published in the last three months. The figure below is from the first article featured in this newsletter.

Article: synthetic data and interpretable machine learning. This first article in a new series on synthetic data and explainable AI, focuses on making linear regression more meaningful and controllable. Includes synthetic data, advanced machine learning with Excel, combinatorial feature selection, parametric bootstrap, cross-validation, and alternatives to R-squared to measure model performance. The full technical article (PDF, 13 pages, with detailed explanations and […]. Read more here.

New book: Interpretable Machine Learning. Subtitled “A Guide for Making Black Box Models Explainable”. Authored and self-published by Christoph Molnar, 2022 (319 pages). This is actually the second edition, the first one was published in 2019. According to Google Scholar, it was cited more than 2,500 times. So this is a popular book about a popular topic. General Comments The […]. Read my review here.

New book: Efficient Deep Learning. Subtitled “Fast, smaller, and better models”. This book goes through algorithms and techniques used by researchers and engineers at Google Research, Facebook AI Research (FAIR), and other eminent AI labs to train and deploy their models on devices ranging from large server-side machines to tiny microcontrollers. The book presents a balance of fundamentals as well […] Read more here.

New book: Probabilistic Machine Learning. By Kevin Murphy, MIT Press (2022). This is one of the best machine learning books that I purchased in the last few years. Very comprehensive, covering a lot of statistical science too. The level is never too high, despite a few advanced concepts being discussed. There is a lot of focus on applications, especially image […] Read my review here.

Browse the MLTechniques.com blog by category to find more content that is relevant to you. For instance, articles in the synthetic data category can be found here. The resources section, here, features detailed technical reports and other books, some available to subscribers only, some available to all.