Normal Equation is an analytical approach to Linear Regression with a Least Square Cost Function. We can directly find out the value of θ without using Gradient Descent. Following this approach is an effective and a time-saving option when are working with a dataset with small features.
Normal Equation is a follows :
In the above equation,
θ : hypothesis parameters that define it the best.
X : Input feature value of each instance.
Y : Output value of each instance.
Maths Behind the equation –
Given the hypothesis function
n : the no. of features in the data set.
x0 : 1 (for vector multiplication)
Notice that this is the dot product between θ and x values. So for the convenience to solve we can write it as :
The motive in Linear Regression is to minimize the cost function :
xi : the input value of iih training example.
m : no. of training instances
n : no. of data-set features
yi : the expected result of ith instance
Let us representing cost function in a vector form.
we have ignored 1/2m here as it will not make any difference in the working. It was used for the mathematical convenience while calculation gradient descent. But it is no more needed here.
xij : value of jih feature in iih training example.
This can further be reduced to
But each residual value is squared. We cannot simply square the above expression. As the square of a vector/matrix is not equal to the square of each of its values. So to get the squared value, multiply the vector/matrix with its transpose. So, the final equation derived is
Therefore, the cost function is
So, now getting the value of θ using derivative
So, this is the finally derived Normal Equation with θ giving the minimum cost value.