Grid search

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

Explains exhaustive combinations, search-grid structure, pipeline naming, validation, and computational limits without detailing the full implementation.

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

Grid search is an exhaustive hyperparameter optimization technique that evaluates every combination of specified hyperparameter values.

Grid search is an exhaustive hyperparameter optimization technique: define candidate values, test every combination, and select the combination with the best cross-validated result. It works best when the number of hyperparameters is small, values are discrete, and tests can run in parallel on a manageable dataset. section 6.3.1

A practical grid-search workflow includes:

Choose a sequence of candidate values for each hyperparameter. section 6.3.1
Represent one or more search spaces as dictionaries mapping parameter names to candidate values. section 6.3.1
Use pipeline parameter names such as model__penalty when tuning a model inside a pipeline. section 6.3.1
Compare the resulting cross-validated scores and retain the best settings. section 6.3.1

Logistic-regression tuning

A Scikit-learn pipeline can test no penalty, L1 or L2 regularization across several C values, and elastic net across C and l1_ratio values. In the demonstrated run, the best result uses no penalty and achieves a cross-validated score of about 0.821. section 6.3.1

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Also explained in

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Grokking Machine LearningA model-selection technique that evaluates an exhaustive set of specified hyperparameter combinations.
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Evolutionary Deep Learning: Genetic algorithms and neural networksGrid search is a hyperparameter-optimization technique that evaluates configurations formed from a specified Cartesian product of candidate values.
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Designing Deep Learning Systems: A guide for software engineersA hyperparameter search method that evaluates configurations formed from the Cartesian product of specified candidate values.
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Bayesian Optimization in ActionGrid search is an optimization technique that evaluates points arranged on a regular grid over the search space.
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Ensemble Methods for Machine LearningGrid search is a model-selection method that evaluates combinations from a specified set of parameter values to identify a performant configuration.

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Related concepts

hyperparameteruses · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
cross-validationuses · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.
random searchcontrasts with · Random search is an optimization technique that explores a search space by evaluating candidate solutions or configurations selected through randomized choices.
Bayesian optimizationcontrasts with · Bayesian optimization is a sequential method for optimizing an expensive or unknown objective by using a probabilistic model to guide evaluations.
linear regressionused by · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

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