What does the hypothesis space mean in Machine Learning?

This set of observations can be used by a machine learning (ML) algorithm to learn a function f that is able to predict a value y for any input from the input space. We are searching for the ground trouth f(x) = y that explains the relation between x and y for all possible inputs in the correct way. read more

For machine learning this means that the larger the hypothesis space, the more data we need to be confident that the best function will actually generalize to data we haven’t seen. read more

A learning algorithm comes with a hypothesis space, the set of possible hypotheses it can come up with in order to model the unknown target function by formulating the final hypothesis Classifier: A classifier is a special case of a hypothesis (nowadays, often learned by a machine learning algorithm). read more

The function f has to be choosen from the hypothesis space. To get a better idea: The input space is in the above given example , its the number of possible inputs. The hypothesis space is because for each set of features of the input space two outcomes (0 and 1) are possible. read more