Linear regression performs the task to predict a dependent variable value( y>
based on a given independent variable( x>
. So, this regression technique finds out a linear relationship between x( input>
and y( output>
. Hence, the name is Linear Regression.
In the figure above, X( input>
is the work experience and Y( output>
is the salary of a person. The regression line is the stylish fit line for our model.
Hypothesis function for Linear Regression
While training the model we're given
x input training data( univariate – one input variable( parameter>
y labels to data( supervised learning>
When training the model – it fits the best line to predict the value of y for a given value of x. The model gets the best regression fit line by finding the best θ1 and θ2 values.
θ2 coefficient of x
Once we find the best θ1 and θ2 values, we get the best fit line. So when we're finally using our model for prediction, it'll predict the value of y for the input value ofx.