Variables in Machine Learning

- Overview

Variables are characteristics that can be measured and can take on different values. They can be found in a research question or hypothesis. 

In mathematics, a variable (from Latin variabilis, "changeable") is a symbol that represents a mathematical object. A variable may represent a number, a vector, a matrix, a function, the argument of a function, a set, or an element of a set. 

Variables are categorized in a variety of ways, including: 

• Independent variables: A variable that stands alone and is not changed by other variables. For example, a person's age.
• Dependent variables: A variable that changes as a result of the independent variable. Also called response variables. For example, how much a dog eats.
• Continuous variables: A variable that can take any value between two numbers. For example, the height of a group of basketball players.
• Discrete variables: A variable that takes on distinct, countable values.
• Confounding variables: A factor other than the one being studied that is associated with both the dependent and independent variables. A confounding variable may distort or mask the effects of another variable.

Other types of variables include: quantitative variables, qualitative variables, intervening variables, moderating variables, extraneous variables.

Please refer to Wikipedia: Variable for more details.

- Identifying Variables

Identifying variables before conducting an experiment is important for a few reasons:

• Define and measure factors: Identifying variables helps researchers clearly define and measure the factors being studied. This improves the reliability and validity of the research findings.
• Select appropriate methods: Understanding variables helps researchers select appropriate research methods and statistical analyses.
• Know what to experiment on: Identifying variables helps researchers know which items to experiment on and which to measure and get results from.
• Identify confounding variables: Identifying confounding variables helps ensure that the relationship being observed between independent and dependent variables is real, and that the results of a study are valid.
• Control variables: Control variables help ensure that the experiment results are fair, unskewed, and not caused by your experimental manipulation. For example, having the same glassware for all experiments is a controlled variable.
• Take variables into account: When scientists are aware of all variables, they can take them into account as they try to make sense of their results.  

- Variables in Machine Learning

In machine learning, data variables, also known as feature variables, attributes, or predictors, are measurable pieces of data that are the basic building blocks of datasets. They are used as input for training and making predictions, and are the columns of the data matrix. 

In machine learning, a variable refers to a feature or attribute used as input for training and making predictions. In this post, we describe the different types of variables (numerical, categorical, etc.) and their possible uses within a model (input, target, etc.).

- Feature Variables in Machine Learning

In machine learning, data variables, also known as feature variables, attributes, or predictors, are measurable pieces of data that are the basic building blocks of datasets. They are used as input for training and making predictions, and are the columns of the data matrix.