Machine Learning: Explanation of Simple Linear Regression Algorithm
A branch of artificial intelligence
What is Linear Regression?
The main working function of the linear regression algorithm is to find the linear relationship between the independent and dependent variables. In machine learning the data is visualized with graphs and gives the intuition of dependency of one variable on another variable in a regress manner i.e. the procedure can be reapplied on the same method. Here, our method is a linear estimator that estimates the result of linear combination.
The estimator is a kind of method or formula with which we can estimate something.
If we break the linear regression into two words i.e. linear is a straight line and regression is a way to go back to the previous state. But our real-life data is not perfect to be linear so the regress method is trying to be perfect with some error.
The equation of a straight line can be in a standard form or with slope-intercept form. For linear regression in classic machine learning, we will be more focused on slope-intercept form as shown below.
Y = mx + c or Y = θ1x + θ0Where,
m or θ1 = Slope of the line
