Model selection

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

Model selection is the process of choosing a model’s architecture and training-related configuration for a problem.

Model selection chooses a model configuration suited to a machine-learning problem. It can reduce the time spent comparing candidate models and expose choices that deserve further tuning. section 1.4.1

Treat model selection as broader than choosing a layer type:

• Compare model types, deep-learning variations, and model ensembles for the same problem. section 1.4.1
• Choose or tune the model architecture, including the types of layers used. section 1.4.1 section 1.5.1
• Search training-related choices such as initial weights, optimization mechanisms, and loss functions. section 1.5.1
• Use automated methods when manual comparison or hyperparameter tuning becomes costly and error-prone. section 1.4.1

A broader search space

A model-selection experiment can tune initial weights, optimization choices, and loss measures alongside layer types, producing a configuration that learns more efficiently for a particular problem. section 1.5.1

Figure 1.3 Steps for developing a good AI/ML model with AutoML and/or EDL

Use the workflow figure to inspect how model selection fits among the steps for developing an AI/ML model with AutoML or evolutionary deep learning. section 1.4.1

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Defines candidate-model and configuration choices, then identifies weights, optimization, and loss as tunable elements without detailed search procedures.

Examples

Uses model comparisons, ensembles, layer choices, initial weights, optimization mechanisms, loss measures, and an AutoML workflow figure.

What you can do after

Apply it via AutoML to compare candidate models and tune architectural and training-related choices for a problem.

How it connects

Connects model selection with AutoML, model architecture, hyperparameter optimization, feature engineering, and validation selection.

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Also explained in

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  Delivering Machine Learning Projects: From design to deploymentModel selection is the process of choosing among candidate machine learning models based on evaluation results and project requirements.
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  Distributed Machine Learning PatternsThe process of choosing a machine learning model or configuration from a set of candidates according to an evaluation criterion.
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  Sutskever's ListThe process of choosing among candidate models according to an evaluation criterion such as description length or predictive performance.
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  Machine Learning Platform EngineeringModel selection is the process of choosing among candidate machine learning models or architectures based on their suitability and measured performance.
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  Demand Forecasting Best PracticesModel selection is the process of choosing a model structure or algorithm that is suitable for representing relationships in data and producing predictions.
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  Data Analysis with LLMsModel selection is the process of choosing a model based on task requirements, performance, cost, data sensitivity, and deployment constraints.
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  Financial AI in PracticeModel selection is the process of choosing among candidate predictive models according to performance and other requirements.
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  Building Reliable AI SystemsThe process of choosing an AI model for a particular application or task.

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Related concepts

• parameter estimationcontrasts with · Parameter estimation is the process of inferring unknown numerical model parameters from observed data and, where applicable, substantive assumptions.
• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• cross-validationimplemented by · 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.
• fine-tuningpart of · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• ensemblepart 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.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.