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\lecture{Machine Learning}{HW01: Decision trees}{CS 689, Spring 2015}

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Hand in via moodle at: \url{https://moodle.umass.edu/course/view.php?id=20836}.
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\bee
\i How many decision trees are there with $3$ binary attributes? With $4$?
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%  There are a lot of trees in the forest.
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\i In class we looked at an example where all the attributes were binary (i.e., yes/no valued). Consider an example where instead of the attribute ``Morning?", we had an attribute ``Time" which specifies when the class begins. 
\bee
\i We can pick a threshold $\tau$ and use (Time $< \tau$)? as a criteria to split the data in two. Explain how you might pick the optimal value of $\tau$.
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%   $\tau = \argmin (f(x))$
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\i In the decision tree learning algorithm discussed in class, once an binary attribute is used, the subtrees do not need to consider it. Explain why when there are continuous attributes this may not be the case.
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\ene
 
\i Give two reasons why memorizing the training data and doing table lookups is a bad strategy for learning.
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% "if you tell the truth, you don't have to remember anything" - Mark twain
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\ene

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