Feature engineering

As explained in Distributed Machine Learning Patterns

Feature engineering is the process of creating, transforming, or selecting input features to improve a machine learning model.

Feature engineering creates derived inputs before fitting a model. Combining overlapping audiovisual features can improve performance, although extensive combinations may make training impractical on a single GPU. section 3.2.2

Use feature engineering to:

• Combine features from related entities when they provide similar or overlapping information. section 3.2.2
• Balance potential model-performance gains against the cost of exploring many feature combinations. section 3.2.2
• Recognize that deep learning models can learn relationships among features through trained weights and biases. section 3.2.2

Animal and Pet video entities

Audiovisual features from Animal and Pet can be combined because the entities overlap. The resulting derived feature may improve classification, depending on the selected model. section 3.2.2

Figure 3.4 Example videos that belong to the Pet entity (Source: Sudheendra Vijayanarasimhan et al. Licensed under Nonexclusive License 1.0)

Inspect Pet videos alongside the Animal examples to identify relationships that may motivate derived features. section 3.2.2

How Distributed Machine Learning Patterns covers it

How deep it goes

Explains derived-feature construction and its performance rationale, then stops short of a procedural feature-engineering workflow.

Examples

Uses overlapping Animal and Pet audiovisual features and a 10-class LeNet classification scenario.

What it assumes

Assumes familiarity with model fitting and audiovisual entity features, while contrasting traditional feature engineering with deep learning.

What you can do after

Evaluate whether combining overlapping audiovisual features is worthwhile before training, especially when exploration threatens single-GPU training time.

How it connects

Connects derived features with model selection, dataset exploration, traditional machine learning, and deep learning.

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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 Distributed Machine Learning Patterns. The section links point to the parts of the book it draws on.