purpose of restricting hypothesis space in machine learning
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The most common form of restriction of hypothesis space is on the grounds of "language bias".
Restriction is always based on some kind of bias.
In machine learning, a hypothesis space is restricted so that these can fit well with the overall data that is actually required by the user.
It checks the truth or falsity of observations or inputs and analyses them properly.
Hence it is very useful and it performs the useful function of mapping of all the inputs till they come out as outputs.
Hence the target functions are carefully analysed and restricted based on the results(whether they are free of bias), in machine learning.
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