Data Types and Level of Measurement in Machine Learning

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#Machine Learning #Feature Engineering #Statistics #Data Types #Basics

Types of Data

There are several debatable categorization methods of data.

The first widely spread theory, or level of measurement, is by S. Stevens. The theory categorizes data into four types, nominal, ordinal, interval, and ratio.

Other methods are proposed for other fields of research. For example, N. R. Chrisman proposed a different method for cartography. However, these are not generic enough for data science. They are more general than a specific field of research.

For machine learning, many statistical data types have been proposed. Some examples of data types and their relations with the level of measurement are shown in the following chart.

Data types and level of measurement. on plectica

Data types and level of measurement. on plectica

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References:
  1. Level of Measurement
  2. Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677.
  3. Chrisman, N. R. (1998). Rethinking Levels of Measurement for Cartography. Cartography and Geographic Information Science, 25(4), 231–242.
  4. Statistical data type
Dynamic Backlinks to wiki/machine-learning/feature-engineering/data-types:
Dealing with Missing Data in Machine Learning
During feature engineering, we have to deal with missing values.

L Ma (2020). 'Data Types and Level of Measurement in Machine Learning', Datumorphism, 01 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/feature-engineering/data-types/.