Hi
Please see below screen shot, Suppose i have data and below blue line best fitted line.
What are techniques to optimize the error e
Thanks,
Srihari
Hi, Srihari.
From the formula you can see that it is a “Mean squared error” we are calculating, by which we are trying to fit a line which is having the minimum error from the true value and predicted value.
If Y’i is the predicted value of the i-th sample, and Yi is the corresponding true value(Ground truth),
Then the mean squared error (MSE) estimated over n samples is defined as
MSE (Y,Y’) = [ Vi (Summation ( Yi - Y’i) ^ 2 ] / n samples .
Where Vi means for all i starting from 0 to (n-1) samples.
There are other types of loss also that you will study in course.
Mean Absolute Error, Root Mean Squared Error and R Squared.
All the best!