Feature engineering

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

Feature engineering is the construction, transformation, or selection of input variables that represent useful information for a machine-learning model.

Feature engineering extracts relevant features from data using domain knowledge, helping machine-learning systems represent useful information for training. Automating it can reduce reliance on expensive, subjective expert judgment while improving standardization. section 1.4.1

Within an automated machine-learning workflow, feature engineering:

• Follows data preparation and can be included as part of an AutoML process. section 1.4.1
• Replaces or standardizes expert selection of relevant features based on intuition and experience. section 1.4.1
• Can be optimized alongside hyperparameters, model options, and model architecture when developing deep-learning systems. section 1.5.6

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

Use the workflow figure to place feature engineering among the broader 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 expert-driven feature extraction and its automation, but defers implementation details to later treatment.

Examples

Uses expert selection of relevant features and a workflow figure showing feature engineering among AutoML and evolutionary deep-learning steps.

What it assumes

Assumes familiarity with deep learning while introducing feature engineering as part of a broader optimization process.

What you can do after

Apply feature engineering within AutoML and deep-learning optimization workflows.

How it connects

Connects feature engineering to data preparation, hyperparameter optimization, model selection, model architecture, AutoML, and evolutionary computation.

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Related concepts

• foundational modeluses · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
• feature selectionhas part · Feature selection is the process of choosing a subset of input features for use in a modeling, analysis, or prediction task.
• data layerpart of · A data layer is an architectural part of a system that organizes access to, management of, or persistence of data for other components.
• pipelinepart of · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
• language modeladdressed by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• signalproduces · A signal is an observable value, event, or pattern that conveys information about a condition, state, or phenomenon.

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.