I am a newbie to machine learning and have the following elementary questions.
Given a labeled dataset with multiple features, is it for a ML algorithm determine on its own what features are to be considered for determining the hypothesis function? Or is it for a user who is training the algorithm to explicitly indicate what features are to be considered?
Is there a mapping between problem space versus the hypothesis function type? As an example based on past experience, can we confidently state that for a housing price prediction problem, linear equation is appropriate. While for determining diabetes based on a given dataset, a quadratic hypothesis equation has proven reasonable.
Or is it usual for a designer to specify a custom function and allow the ML algo to determine the coefficients of this custom function (e.g. square root of sum of squares of 9 of 15 features divided by e^n) by reducing the error using gradient descent?