How deeply does The Art of AI Product Development cover gradient boosting?

Compares practical performance and runtime, but stops short of explaining boosting mechanics or implementation details.

Gradient boosting

As explained in The Art of AI Product Development

A gradient boosting algorithm builds an ensemble model by sequentially adding weak predictive models that reduce errors made by the existing ensemble.

Gradient boosting is a practical alternative when a deep learning model is computationally expensive but demand forecasts already meet an acceptable error range. Its value is measured against client needs, not accuracy alone. section 12.1.2

Use a business benchmark to frame the choice: compare training and runtime with predictive performance, then determine whether the simpler approach stays within the agreed tolerance for most datasets. section 12.1.2

Demand forecasting under a 5% error target

A team targeting forecasts with an error margin of ±5% considers gradient boosting instead of deep learning. Initial tests suggest it trains and runs faster while remaining within the threshold for most datasets, so the team compares runtime and performance metrics before deciding. section 12.1.2

How The Art of AI Product Development covers it

Cover of The Art of AI Product Development

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA decision-tree ensemble model built by sequentially adding trees to optimize a predictive loss using gradient-based optimization.
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Ensemble Methods for Machine LearningGradient boosting is an ensemble-learning technique that sequentially adds models trained to correct the errors of prior models using gradient-based optimization.
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Grokking Machine LearningAn ensemble model that sequentially combines decision trees, with each tree improving the ensemble's fit to the data.
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Machine Learning System DesignA gradient-boosting model is an ensemble model built by sequentially fitting weak predictive models to reduce the errors of the current ensemble.

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Related concepts

XGBoostimplemented by · XGBoost is an open-source software library for efficient gradient-boosted decision-tree algorithms used in supervised machine learning.
Newton boostingcontrasts with · Newton boosting is a boosting approach that uses first- and second-order loss information to construct successive learners.
ensemblekind of · An ensemble is a technique that combines or considers outputs from multiple models or component methods collectively to produce or assess a joint result.
random forestalternative to · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
decision treehas part · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate their probabilities and predict or generate sequences of them.

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