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#1




The concept "h is fixed before you generate the data set" is extremely vague
Can someone please explain to me the concept of "h is fixed before you generate the data set" as appears on page 22 of the text?
As it stands, this is an extremely vague statement. What does it mean by "fixed", what does it mean by "generate"? Here is a typical modern machine learning pipeline for most students. Find some data somewhere, typically Kaggle (you don't generate it whatsoever, someone else does it for you through unknown means) Observe the data, get a sense of its dimensionality, number of data. If data is too large, cannot even load into a computer. Therefore parameters associated with this data MUST be known in order to do machine learning. Based on the data, categorize it into a typical problem. For example, classification, prediction, etc. Pick a hypothesis h known to do well for the problem. Say SVM. Tune the hypothesis h so that it can at least accept the data. For example, the dimensionality of the weights in the hypothesis is obtained from the dimensionality of the data. Otherwise, a dimension mismatch error will be thrown by MATLAB and no machine learning can be done. Train your hypothesis h, parameterized by the weights w, until h achieves the lowest insample error. Call that the final hypothesis g. Use final hypothesis g on test set. In this pipeline, data is not generated, it is given. h is not fixed, it is adjusted based on the data (type of data, dimensionality of data). If we do not know the data at all, we cannot possibly construct a hypothesis. It would be akin to using a lowpass filter for 1D signals when your data is actually a continuous stream of 3D video frames. The data must be given prior to constructing h, and h must be adjusted based on the problem at hand. This is not a "before", it is clearly an "after". Why does it seem that this typical learning pipeline do not fit into the learning model described in the book? What does it mean by "h is fixed before you generate the data set" in a practical sense? 
#2




Re: The concept "h is fixed before you generate the data set" is extremely vague
Good question. Yes, the statement on page 22 does not fit into the actual learning scenario yet, as explained in your words and similarly on page 23. If you read on, you'll gradually see how we move closer to the actual scenario. What page 22 tries to say is that the fixed h (i.e. a readilycolored bin) is the assumption that the bin model needs. The closest realworld scenario is perhaps when someone hands you a hypothesis before anyone looks at the data (generated by someone else, say, on Kaggle). If you assume that the data generator gathers/generates the data i.i.d. from some distribution, you can *test* the hypothesis using the results on page 22.
Hope this helps.
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#3




Re: The concept "h is fixed before you generate the data set" is extremely vague
Actually this is more intuitive than it may seem. Going back to the bin model: What you need to remember is that the choice of any hypothesis h determines the color of the marbles in the bin (h(x)=f(x) then green otherwise red).
So suppose you've chosen a particular h, after that you then select a sample out of the bin (this is what is meant with "generate"). Because of probability properties we know that the contents of the bin will be similar to the sample with a certain likelihood. Now, however, suppose we change the h after we have selected (generated) the sample. What this means is that we are recoloring the marbles in the bin according to the new h. The sample that you had previously selected knows absolutely nothing about (has absolutely no relation to) this new recoloring. The marbles could be all green or all red or anything else in between depending on the new h. So for example if they were all green and you selected a new sample then that new sample would be all green. In short, you can't do a selection out of the bin, change the contents of the bin arbitrarily and expect that selection to be able to say something about the new contents of the bin. 
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