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Free Guide: Interpretable Machine Learning

A Guide for Making Black Box Models Explainable, by Christoph Molnar.
Machine learning has a huge potential to improve products, processes and research. But machines usually don’t give an explanation for their predictions, which hurts trust and creates a barrier for the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.

Machine learning models are already used to choose the best advertisement for you, it filters out spam from your emails and it even assesses risk in the judicial system which ultimately can have consequences for your freedom. Can everyone trust the learned model? The model might perform well on the training data, but are the learned associations general enough to transfer to new data? Are there some oddities in the training data which the machine learning model dutifully picked up? This book will give you an overview over techniques that you can use to make black boxes as transparent as possible and make their predictions interpretable. The first part of the book introduces simple, interpretable models and instructions how to do the interpretation. The later chapters focus on general model-agnostics tools that help analysing complex models and making their decisions interpretable. In an ideal future, machines will be able to explain their decisions and the algorithmic age we are moving towards will be as human as possible.

This books is recommended for machine learning practitioners, data scientists, statisticians and anyone else interested in making machine decisions more human.

Free Guide: Interpretable Machine Learning

About me: My name is Christoph Molnar, I am something between statistician and machine learner. I work on making machine learning interpretable. If you are interested in bringing interpretability to your machine learning models, feel free to contact me!
Most common arguments against interpretable ML:

  • Humans can’t explain their actions either
  • Performance > Interpretability
  • Slows down ML adoption
  • Linear model also not interpretable
  • Might create illusion of understanding the model

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