Correlation Analysis In Machine Learning

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Correlation Analysis

Correlation is used to test relationships between quantitative variables or categorical variables. In other words, it’s a measure of how things are related. The study of how variables are correlated is called correlation analysis.

Some examples of data that have a high correlation:

  • Your caloric intake and your weight.

  • Your eye color and your relatives’ eye colors.

  • The amount of time your study and your GPA.

What is Correlation Analysis?

Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables (e.g. height and weight). This particular type of analysis is useful when a researcher wants to establish if there are possible connections between variables.


Some examples of data that have a low correlation (or none at all):

  • Your sexual preference and the type of cereal you eat.

  • A dog’s name and the type of dog biscuit they prefer.

  • The cost of a car wash and how long it takes to buy a soda inside the station.

Types Of The Correlation Coefficient

Correlation coefficients have a value of between -1 and 1. A “0” means there is no relationship between the variables at all, while -1 or 1 means that there is a perfect negative or positive correlation


The most common correlation coefficient is the Pearson Correlation Coefficient. It’s used to test for linear relationships between data.

Others are: Goodman and Kruskal’s lambda coefficient is a fairly common coefficient. It can be symmetric, where you do not have to specify which variable is dependent, and asymmetric where the dependent variable is specified.