Correlation Analysis In Machine Learning
What type of projects or assignments help looking for?
Assignment or Project Help
Online Training and Mentorship
New Idea or project
Existing project that need more resources
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.