How deeply does Financial AI in Practice cover XGBoost?

Places XGBoost after WoE feature transformation but does not detail its training mechanics.

XGBoost

As explained in Financial AI in Practice

XGBoost is an open-source software library for gradient-boosted decision trees.

XGBoost is a tree-based model used for credit scoring. In a manual WoE workflow, it serves as a baseline trained on WoE-coded features. section 5.2.3 section 5.3.1

A credit-scoring workflow places XGBoost after data preparation and feature transformation:

Clean and explore the tabular data, then calculate WoE and IV and select features. section 5.2.3
Train a baseline XGBoost model on WoE-coded features; evaluate performance with cross-validation and confusion matrices. section 5.2.3

How Financial AI in Practice covers it

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA predictive model trained using XGBoost's gradient-boosted decision-tree algorithms.
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Ensemble Methods for Machine LearningXGBoost is an open-source machine-learning library implementing scalable gradient-boosted decision-tree algorithms.
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Grokking Machine LearningAn open-source software library that implements optimized gradient-boosted decision trees for machine-learning tasks.
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Machine Learning Platform EngineeringAn XGBoost model is a gradient-boosted decision-tree model trained using the XGBoost algorithm.
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Delivering Machine Learning Projects: From design to deploymentXGBoost is an open-source implementation of gradient-boosted decision trees for efficient supervised learning.

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Related concepts

decision treeuses · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
gradient boostingimplements · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
weighted quantile sketchuses · A weighted quantile sketch is an approximate algorithm for estimating quantiles or quantile-based split points while accounting for item weights.
early stoppinguses · Early stopping is a technique that terminates an iterative process before its planned completion when a specified condition indicates that further work is unnecessary or unlikely to improve the objective.
LightGBMcontrasts with · LightGBM is an open-source gradient-boosting framework for efficiently training tree-based machine-learning models.

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.