How deeply does Machine Learning Algorithms in Depth cover boosting?

Explains sequential weighting, weighted errors, and prediction combination, while stating gradient boosting’s loss minimization without fully deriving it.

Boosting

As explained in Machine Learning Algorithms in Depth

Boosting is an ensemble learning technique that combines weak learners trained sequentially with greater emphasis on previously misclassified examples.

Boosting combines weak learners into a stronger predictive model by training them sequentially. Each round emphasizes examples misclassified earlier, then combines predictions using a weighted vote for classification or weighted sum for regression. section 7.5 section 7.5.2

AdaBoost operationalizes the idea through an adaptive sequence:

Initialize all training-example weights equally and fit the first weak classifier. section 7.5.2
Increase weights for misclassified examples and decrease weights for correctly classified examples in later rounds. section 7.5.2
Measure each learner’s weighted error and assign more influence to more accurate learners. section 7.5.2

Decision stumps provide a concrete base learner: each depth-one tree partitions feature space with a threshold. Gradient tree boosting extends the sequential strategy to differentiable loss functions for classification and regression. section 7.5.2 section 7.5.2

AdaBoost on feature data

A Python implementation trains depth-one decision trees on two Iris features, compares ensembles with 1, 2, 3, and 10 learners, and can also illustrate the method on an XOR dataset. Learning curves and decision regions show how accuracy changes as learners are added.

How Machine Learning Algorithms in Depth covers it

Cover of Machine Learning Algorithms in Depth

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Also explained in

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Grokking Machine LearningAn ensemble technique that trains learners sequentially so later learners focus on weaknesses of earlier learners and combines their predictions.
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Ensemble Methods for Machine LearningBoosting is an ensemble technique that combines weak learners trained sequentially so that later learners improve on earlier mistakes.

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Related concepts

weak learneruses · A weak learner is a predictive model that performs only modestly better than chance or a simple baseline and can contribute to a stronger ensemble.
gradient boostingincludes · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
AdaBoostincludes · AdaBoost is an ensemble-learning algorithm that sequentially combines weak learners, adaptively reweighting training examples so that later learners emphasize those earlier learners handled poorly.
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.
baggingcontrasts with · Bagging, or bootstrap aggregation, is an ensemble technique that trains multiple models on bootstrap samples of data and combines their predictions.
strong learnerproduces · A strong learner is a predictive model with substantially improved task performance, often formed by combining multiple weak learners.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.