Unstructured data

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

Data whose varied modalities and organization do not conform to a fixed rows-and-columns schema.

Unstructured data contains varied modalities, file types, and information that do not fit a consistent rows-and-columns model. Common examples include images, video, audio, text, sensor data in JSON, and social media streams. section 1.1 section 1.4

A predictive workflow traditionally converted unstructured inputs into structured features through feature engineering. Deep learning can instead learn the necessary transformations end to end, from the original input to a prediction. section 1.4

Use the distinction from tabular data to guide modeling choices:

• Expect unstructured data to arrive as different files or instances containing information in an unordered form rather than as a matrix. section 1.4
• Recognize that structured data is broader than tabular data: nested JSON, relational, time-series, graph, and spatial data may have structure without sharing one rows-and-columns format. section 1.1
• Choose deep learning when the problem benefits from learning representations directly from modalities such as images, audio, video, or text. section 1.4

Figure 1.1 An example of tabular data

Use the tabular-data figure as a visual contrast for identifying when inputs do not conform to a regular rows-and-columns layout. section 1.1

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes

Defines the format, lists modalities, explains feature engineering, and reaches end-to-end representation learning without implementation details.

Examples

Uses images, video, audio, text, JSON sensor data, and social media streams as representative inputs.

What it assumes

Assumes a basic contrast with tabular data and its regular rows-and-columns representation.

What you can do after

Evaluate whether a dataset requires handcrafted feature extraction or can support end-to-end deep-learning transformations.

How it connects

Connects unstructured data with tabular data, structured data, feature engineering, machine learning, and deep learning.

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Related concepts

• structured datacontrasts with · Structured data is information organized according to an explicit schema or predictable fields, types, records, or relationships for consistent machine processing.
• textincludes · Text is written or digitally encoded linguistic content represented as characters, words, tokens, or other textual units.
• retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
• language modelused 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.
• deep learningused by · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.