The purpose of the exercise is to show what can
possibly happen outside the data, namely anything, and it is the same no matter how you pick your
from the data.
Compare this with the bin in the next section. What can
possibly remain in the bin after you pick your sample is every possible combination of red and green marbles.
So it is always *possible* to end up with a hypothesis that is arbitrarily bad no matter what algorithm you use to pick that hypothesis using only the data as a guide. That is what thiis exercise illustrates. In the next section you will learn that while anything is possible, some things are more likely than others.
Quote:
Originally Posted by netweavercn
A little confused about 1.7. what is the purpose of this exercise: learning is impossible?
in (a), both hypotheses have 1 agree on all 3 points and no agree on all other 7 cases. so which hypothesis should be chose by learning algorithm? as the 2 hypotheses are the same.
in(b), still same situation
in(c), there are 4 cases agrees.
(d), is it possible to agree all 8 cases?

You may have misunderstood the question. In (a) you pick the hypothesis that is always
, since that is what agrees with the data the most (from the table on the same page). Compare this
with the possible choices for
given by
: 1 agrees with
on all three test points; 3 agree with
on two test points; 3 agree with
on one test points; 1 agrees with
on none of the test points;