Objective Function and Regularization etc (ML)
Regression While learning ML algorithms, one often comes across new concepts and terms .In this post, I try to cover some notes on optimization, cost function, regularization w.r.t regression. The notes are collated for easy access and reference from the rich content made available by experts. To fit a model, it often boils down to finding an equation that passes through all (most) of the training data points. Say if it’s a linear plane, one tries to find a line that goes through all the points. Once an initial line is drawn, we use different distance measures to find it it’s the best line or not. Say the line is given by equation below The problem boils down to how to find the value of such that for values of x, predicted values of y is closer to actual value of y . In regression, OLS is a widely used cost function, where we try to find the line such that sum of square of distances of points and the line is minimum. Optimization and Cost Function