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Model-based analysis

As explained in Delivering Machine Learning Projects: From design to deployment

Model-based analysis uses a model to transform or summarize data so that its properties can be interpreted.

Model-based analysis uses a foundational model to map data into a summary or visualization domain, making its properties easier to interpret. section 6.1.1

Within exploratory data analysis, it complements summary statistics and visualizations. For unstructured images or text, it reduces real-world features into a human-interpretable format. section 6.1.1

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines the transformation and its purpose but stops short of specifying model architectures, training procedures, or evaluation methods.
Examples
Uses images and text as unstructured data whose features require reduction into interpretable summaries or visualizations.
What it assumes
Assumes familiarity with exploratory data analysis and foundational models, while identifying why unstructured data needs model-based reduction.
What you can do after
Evaluate data properties by converting complex or unstructured observations into human-interpretable summaries or visualizations.
How it connects
Connects exploratory data analysis with foundational models, summary statistics, visualizations, and unstructured data.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.