Acquisition function

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover acquisition function?

Explains exploration, exploitation, surrogate feedback, and prior-to-posterior updating without deriving acquisition-function mathematics.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

An acquisition function scores candidate parameter settings according to their expected value and uncertainty for the next objective evaluation.

An acquisition function ranks untried hyperparameter combinations by their promise and uncertainty, guiding the next objective evaluation in Bayesian optimization. section 6.3.4

It balances exploration of uncertain regions with exploitation of combinations expected to improve model performance. A surrogate function supplies the estimates and is updated from objective feedback. section 6.3.4

Optuna hyperparameter search

An Optuna study searches XGBoost settings such as learning rate, estimators, subsampling, depth, and regularization, then records the best parameters and objective value.

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

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Also explained in

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Bayesian Optimization in ActionAn acquisition score is a numerical measure of the expected usefulness of evaluating a candidate point or batch for identifying an objective-function optimum.
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Experimentation for EngineersAn acquisition function is a criterion that scores candidate evaluations according to their expected value for selecting the next experiment in Bayesian optimization.

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Related concepts

lower confidence boundincludes · A lower confidence bound is an acquisition function that combines a predicted response with a multiple of predictive uncertainty to favor promising or informative evaluations.
active learningused by · Active learning is a machine-learning technique in which a learner selectively chooses data examples for labeling or feedback to improve a model.
upper confidence boundused by · Upper confidence bound is a decision rule that combines an estimated value with an uncertainty bonus to select actions or candidates optimistically.
Bayesian optimizationproduced by · Bayesian optimization is a sequential method for optimizing an expensive or unknown objective by using a probabilistic model to guide evaluations.
optimizationused by · Optimization is the systematic selection or adjustment of values, actions, or system designs to improve a specified objective, often subject to constraints.
expected improvementincludes · Expected improvement is a Bayesian optimization acquisition function that estimates a candidate’s expected gain over the best observed objective value while accounting for predictive uncertainty.

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