How deeply does Financial AI in Practice cover SHAP?

Defines feature contributions through a credit example but does not detail calculations or implementation.

SHAP

As explained in Financial AI in Practice

A feature-attribution method that explains a model prediction by assigning each input feature a contribution based on Shapley values.

SHAP (SHapley Additive Explanations) explains an individual credit decision by showing how much each feature contributed to the final score. It supports borrower conversations and helps translate model insights into reason codes. section 6.3.1

Explaining a credit score

An explanation might show that rising revenue contributed positively to an applicant’s score, while a recent increase in revolving credit balance had a larger negative effect. These contributions can support a reason code such as “Excessive credit usage.” section 6.3.1

How Financial AI in Practice covers it

liveBook manning.com

Read Financial AI in Practice on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Ensemble Methods for Machine LearningSHAP is a model-agnostic explainability technique that attributes an individual model prediction to its input features using Shapley values.
liveBook manning.com

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA SHAP value is a feature-attribution value based on Shapley values that quantifies a feature's contribution to an individual prediction.
liveBook manning.com

Machine Learning System DesignA feature-attribution technique based on Shapley values that assigns contributions of input features to a model prediction.
liveBook manning.com

Outlier Detection in PythonSHAP is an open-source Python library for explaining machine-learning model outputs using feature attribution methods based on Shapley values.

Compare how 5 books explain SHAP →

Related concepts

interpretabilityused by · Interpretability is the degree to which people can understand a model’s behavior, outputs, predictions, or the factors that produce them.
Shapley valueuses · A Shapley value is a cooperative-game-theoretic attribution that assigns a participant its average marginal contribution across all possible coalitions.
model-agnostic methodkind of · A model-agnostic method analyzes predictions or estimates explanations without relying on a model’s internal structure, implementation, or architecture.
local explanationused by · A local explanation describes how or why a model produced a particular prediction or decision for an individual input.
feature attributionkind of · Feature attribution is a technique for estimating or assigning the contribution of individual input features to a model’s output.

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.