I am new to machine learning and seek your help in clarifying my elementary doubts. I did a fair amount of googling, but find most literature jumping directly into math.
What I know is that given a labelled training data, a ML algorithm chooses from a hypothesis space H a hypothesis function h.
As an example, assume that a feature vector in training data contains 3 features (x1 through x3)
Now the data from the training set is taken and plugged into a formula (function type). If x is the feature vector and w represents the coefficients of the formula, then output y = A pre-determined f(w,x).
My questions are:
1. Who decides the range of each of the coefficients? 2. Who decides the formula? Is the formula fixed for a ML algorithm? 3. What exactly is the hypothesis space? Is it range of w, or is it different formulas or both?
I acknowledge I have asked more than one question and against the rules, but it was convenient to logically group them in a single post.