Implement the FIND-S algorithm. First verify that it successfully produces the trace in Section 2.4 for the Enjoysport example. Now use this program to study the number of random training examples required to exactly learn the target concept. Implement a training example generator that generates random instances, then classifies them according to the target concept: (Sunny, Warm?, ?, ?, ?) Consider training your FIND-Sprogram on randomly generated examples and measuring the number of examples required before the program's hypothesis is identical to the target concept. Can you predict the average number of examples required? Run the experiment at least 20 times and report the mean number of examples re- quired. How do you expect this number to vary with the number of "?" in the target concept? How would it vary with the number of attributes used to describe instances and hypotheses?
Implement the FIND-S algorithm. First verify that it successfully produces the trace in Section 2.4 for the Enjoysport example. Now use this program to study the number of random training examples required to exactly learn the target concept. Implement a training example generator that generates random instances, then classifies them according to the target concept: (Sunny, Warm?, ?, ?, ?) Consider training your FIND-Sprogram on randomly generated examples and measuring the number of examples required before the program's hypothesis is identical to the target concept. Can you predict the average number of examples required? Run the experiment at least 20 times and report the mean number of examples re- quired. How do you expect this number to vary with the number of "?" in the target concept? How would it vary with the number of attributes used to describe instances and hypotheses?
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Transcribed Image Text:Implement the FIND-S algorithm. First verify that it successfully produces the trace in Section 2.4
for the Enjoysport example. Now use this program to study the number of random training
examples required to exactly learn the target concept. Implement a training example generator
that generates random instances, then classifies them according to the target concept: (Sunny,
Warm?, ?, ?, ?) Consider training your FIND-Sprogram on randomly generated examples and
measuring the number of examples required before the program's hypothesis is identical to
the target concept. Can you predict the average number of examples required? Run the
experiment at least 20 times and report the mean number of examples re- quired. How do you
expect this number to vary with the number of "?" in the target concept? How would it vary with
the number of attributes used to describe instances and hypotheses?
