Linear and Logistic Regression are basically from Statistics but also used in ML. Linear Regression is used for understanding the relationship between input and output numerical variables.
In ML, Its termed as simple model which is a linear predictive model.
Linear Regression as Math equation:
Y = aX + c
Y = Output variable; X = Input Variable ; c = Constant ; a= slope of line.
in ML, we say c= constant and a as weight and then we can predict the value of y for any new x.
So, we draw a random line on the graph for some random value of c and a. Lets say we keep c and a both 1 (c=1, a=1) and draw the line on the graph for each (well at least 2 ) x. Based upon values of x this line might end up in one of the Positions like – little up, down, left, right.
It can then be converted to Probabilistic model called sigmoid function to calculate the Probability.
The difference between Linear regression and Logistic Regression
AI : An Amazing Technology 