Data Types
Discover the different types of data used in machine learning, including quantitative and qualitative forms, and how data is organized in structured, semi-structured, and unstructured formats. Learn how these data types impact model training and performance, equipping you to make informed choices about data handling.
Importance of data in machine learning
The quality of data is important for any ML model. Data is a collection of measurements or observations—mostly in the form of text, symbols, audio, images, and video—used to train, evaluate, and test any ML model. Data is processed in order to turn it into useful information, and the quantity and quality of data used to train any ML model significantly affects its performance. Data is classified into two types, which we will discuss in detail in this lesson.
Quantitative data
Quantitative data, also known as numerical data, includes data that is either quantifiable or expressed in numbers. In other words, it gives information about the quantity of a specific object. For instance, quantitative data can be a human’s height, length, and weight. It can also include an estimation of a person’s age or a measurement of temperature.
Quantitative data is classified into two types: discrete and continuous.
Discrete data
This type of quantitative data uses discrete or whole numbers to represent information. Discrete data cannot be further divided into smaller units. It includes the number of learners in a class, days in a month, the number of players in a ...