Intro:
So in today's post, we're going to look into the Linear Regression algorithm. I'm aiming to break it down into simple terms and summarise it for all of you!
About:
Linear Regression - Models the relationship
Independent variables go on the X axis.
Dependant variables go on the Y axis.
If they both increase, that's a positive relationship.
If as X increases, Y decreases, it's a negative relationship.
It's used to predict values for continuous variables.
Benefit:
Its simplicity is what stands out and makes it better than other ML algorithms.
Limitation:
It makes assumptions on the dependency between the 2 variables that can make it bias.
Final Things:
As always, thank you for making it this far, please leave comments, they help me so much. Be sure to like and share also, lets try and get 100 views within a week, like we have done previously. Follow CompAndCode on all platforms!
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