# What does “no value is acceptable” in Machine Learning hypothesis representation

I am new to machine learning. I am reading the book, where I have: – Most general hypothesis < ? ? ? ? ? ? > – Most specific hypothesis < Ø Ø Ø Ø Ø Ø >

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I understand that for a learning instance, "?" means it can take on any value, so the outcome of the hypothesis would be 1, as long as the specific attribute is met.

I am confused about "Ø". The book says any value is unacceptable.

Does that mean, if a hypothesis is < ? ? ? ? ? Ø>, no matter what first 5 attributes are, since the last one is unacceptable, the outcome of the hypothesis will be 0? If so, what is the difference between < ? ? ? ? ? Ø>, and < Ø Ø Ø Ø Ø Ø >?

• I don't understand your question, but unfortunately I'm not sure you would be able to clarify it, since otherwise you wouldn't be asking the question. – Yuval Filmus Feb 1 '17 at 8:51