A 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 the table, just return d. For inputs that are in the table, return the output associated with it (or the most frequent output. if there is more than one). Implement this algorithm and see how well it does on the restaurant domain. This should give you an idea of the baseline for the domain—the minimal performance that any algorithm should be able to obtain.
Answer to relevant QuestionsSuppose you are running a learning experiment on a new algorithm. You have a data set consisting of 2 examples of each of two classes. Yon plan to use leave-one-nut cross-validation. As a baseline, you run your experimental ...This exercise concerns the expressiveness of decision lists (Section 18.5).a. Show that decision lists can represent any Boolean function, if the size of the tests is not limited.b. Show that if the tests can contain at most ...The data used for Figure can be viewed as being generated by h5. For each of the other four hypotheses, generate a data set of length 100 and plot the corresponding graphs for P (hi│d1... dm) and P (D m + 1 = ...Consider an arbitrary Bayesian network, a complete data set for that network, and the likelihood for the data set according to the network. Give a simple proof that the likelihood of the data cannot decrease if we add a new ...Implement a data structure for layered, feed-forward neural networks, remembering to provide the information needed for both forward evaluation and backward propagation. Using this data structure, write a function ...
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