Let’s have fun with Correlation:-O
Ok have some fun first. 😀
Whenever you will read any post or paper related to Machine-Learning or Data-Science you will get word ‘Correlation’ many times and how it’s value is important in your model Building.
A simple definition of Correlation: A mutual relationship or connection between two or more things. (that’s layman’s definition and It should be enough most of the times 😉 )
Coefficient of Correlation is just an integer, From which we understand how two or more things
are related to each-other. As we discussed Coefficient of Correlation is an integer so it could be +ve or -ve and value of correlation decides how two data-sets effect each other.
Following two images tell lot about Correlation and it’s Value.
Coefficient of Correlation between range -0.5 to +0.5 is not that valuable by why and how we calculate correlation?
What is Covariance ?
Now if you still feel that something is really missing we should talk about Variance:
Let’s Remove Co from Covariance.
Variance is Measurement of randomness. So How you would calculate Variance of Data?
Give me data:
Data = [4,5,6,7,12,20]
I will find means and subtract it from each individual- Isn’t that Mean-Deviation ? 😀 OMG!
Have a look at the following Picture:
Let’s wait for stuff like:
Coefficient of determination, Probable Error and interpretation.