Distance

As explained in Machine Learning System Design

A function that assigns a numerical measure of separation or dissimilarity between mathematical objects or representations.

How Machine Learning System Design covers it

How deep it goes

Contrasts a simple sum with a weighted sum, but does not detail how to choose or train the weights.

Examples

Combines query-to-image and query-to-description distances with user and document features in a photo-search score.

What you can do after

Evaluate whether a simple sum or weighted sum should combine retrieval distances and other features.

How it connects

Connects distance aggregation with feature engineering, relevance scoring, and linear models.

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Also explained in

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  Data Without LabelsA distance metric is a distance function that satisfies the metric-space properties, including nonnegativity, symmetry, and the triangle inequality.
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  Advanced Algorithms and Data StructuresA distance function is a function that assigns a path-cost contribution or distance value to an edge or other argument.
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  Outlier Detection in PythonA function that quantifies dissimilarity or separation between observations in a data space.
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  Natural Language Processing in Action, Second EditionA distance metric is a function that quantifies dissimilarity between objects while satisfying the mathematical properties of a metric.
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  Math for Web DesignDistance is a numerical measure of the separation between two points or geometric objects.
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  Architecture Modernization: Socio-technical alignment of software, strategy, and structureDistance is the amount of technical and organizational coordination required for related parts to implement a change together.
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  Geometry for ProgrammersDistance is a nonnegative measure of separation between points or between a point and a geometric set.
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  Machine Learning Platform EngineeringA distance metric is a function that quantifies separation between vectors in a mathematical space.
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  Classic Computer Science Problems in Swift: Essential techniques for practicing programmersA measure of separation between locations or points in a feature space.

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Related concepts

• mahalanobis distanceincludes · Mahalanobis distance is a covariance-adjusted measure of separation between observations or between an observation and a reference distribution.
• Euclidean distanceincludes · Euclidean distance is the straight-line distance between two points in Euclidean space, calculated as the square root of the sum of squared differences between corresponding coordinates.
• similarity metricmeasures · A similarity measure is a rule that quantifies or characterizes how alike two objects or representations are according to selected properties.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.