XGBoost trains a gradient-boosted decision-tree model and fits into an MLflow workflow that records datasets, parameters, metrics, models, and artifacts for reproducible experimentation. section 4.1.2
XGBoost
An XGBoost model is a gradient-boosted decision-tree model trained using the XGBoost algorithm.
How Machine Learning Platform Engineering covers it
- How deep it goes
- Demonstrates DMatrix construction, binary objectives, hyperparameters, boosting rounds, predictions, and feature-importance plots.
- Examples
- Uses income-classifier train/test datasets, binary logistic and hinge objectives, prediction outputs, and importance plots.
- What it assumes
- Assumes prepared train/test feature and label arrays suitable for conversion into XGBoost DMatrix objects.
- What you can do after
- Apply it via XGBoost and MLflow to train, inspect, and automatically record a binary classification model.
- How it connects
- Connects XGBoost with MLflow tracking, autologging, MinIO datasets, binary classification, and feature importance.
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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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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Financial AI in PracticeXGBoost is an open-source software library for gradient-boosted decision trees.
Compare how 6 books explain XGBoost →
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 Machine Learning Platform Engineering. The section links point to the parts of the book it draws on.