Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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Interpreting models to ensure fairness

In Chapter 8, Privacy, Debugging, and Launching Your Products, we discussed model interpretability as a debugging method. We used LIME to spot the features that the model is overfitting to.

In this section, we will use a slightly more sophisticated method called SHAP (SHapley Additive exPlanation). SHAP combines several different explanation approaches into one neat method. This method lets us generate explanations for individual predictions as well as for entire datasets in order to understand the model better.

You can find SHAP on GitHub at and install it locally with pip install shap. Kaggle kernels have SHAP preinstalled.


The example code given here is from the SHAP example notebooks. You can find a slightly extended version of the notebook on Kaggle:

SHAP combines seven model interpretation methods, those being LIME, Shapley sampling...