Suppose we generate a training set from a decision tree and then apply decision-tree learning to that training set. Is it the case that the learning algorithm will eventually return the correct tree as the training set size goes to infinity? Why or why not’?
Answer to relevant QuestionsA good straw man” learning algorithm is as follows: create a table Out of all the training examples identify which output occurs most often among the training examples; call it d. Then when given an input that is not in ...In the chapter we noted that attributes with many different possible values can cause problems with the gain measure. Such attributes tend to split the examples into numerous small classes or even singleton classes, thereby ...Suppose that FOIL is considering adding a literal to a clause using a binary predicate P and those previous literals (including the head of the clause) contain five different variables.a. How many functionally different ...This exercise investigates properties of the Beta distribution defined in Equation (20.6). a. Dy integrating over the range [0, 1], show that the normalization constant for the distribution beta [a, b] is given by α = ...Suppose you had a neural network with linear activation functions. That is, for each unit the output is some constant times the weighted sum of the inputs.a. Assume that the network has one hidden layer. For a given ...
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