Acquisition function
An acquisition function is a scoring rule that ranks candidate evaluations by their expected usefulness for selecting the next sample or experiment.
Bayesian Optimization in Action
An acquisition score is a numerical measure of the expected usefulness of evaluating a candidate point or batch for identifying an objective-function optimum.
- How it is used
- Guides each Bayesian-optimization query toward an expensive objective’s global optimum by scoring candidate inputs.
- How deep it goes
- Defines scoring and develops iterative, visual, implementation, batch, and information-theoretic uses, while deferring policy-specific details to other treatments.
- Examples
- Uses a one-dimensional objective, GP confidence intervals, Expected Improvement score curves, BoTorch optimization, and clustered parallel queries.
- What you can do after
- Apply it via BoTorch optimize_acqf() to select score-maximizing Bayesian-optimization queries and inspect policy progress.
Read how Bayesian Optimization in Action explains acquisition function →
Experimentation for Engineers
An acquisition function is a criterion that scores candidate evaluations according to their expected value for selecting the next experiment in Bayesian optimization.
- How it is used
- Automates next-experiment selection within Bayesian optimization while balancing metric improvement against surrogate uncertainty.
- How deep it goes
- Defines LCB, explains its trade-off and tuning parameter k, and demonstrates numerical minimization without surveying acquisition-function theory.
- Examples
- Uses a JIT compiler and web-server CPU-time optimization, including a NumPy LCB calculation and parameter values near 0.11 and 0.15.
- What you can do after
- Apply it via Bayesian optimization to select experiments that improve a business metric while reducing uncertainty.
Read how Experimentation for Engineers explains acquisition function →
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.
- How it is used
- Guides Bayesian hyperparameter search toward promising or uncertain parameter combinations for the next model evaluation.
- How deep it goes
- Explains exploration, exploitation, surrogate feedback, and prior-to-posterior updating without deriving acquisition-function mathematics.
- Examples
- Uses an Optuna XGBoost search with parameter listings, optimization history, and estimated hyperparameter importance.
- What you can do after
- Apply it via Optuna to configure and run sequential hyperparameter optimization studies.
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.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.