[amp_mcq option1=”identify the best partition of the input space and response per partition to minimise sum of squares error” option2=”identify the best approximation of the above by the greedy approach (to identifying the partitions)” option3=”identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme” option4=”identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme” correct=”option2″]<\/p>\n
The correct answer is: B. identify the best approximation of the above by the greedy approach (to identifying the partitions)<\/strong><\/p>\n The greedy approach is a heuristic algorithm that constructs a solution to a problem by making the locally optimal choice at each step. In the case of decision tree learning, the greedy approach would start by splitting the data into two sets, each of which contains only one class. It would then continue to split each set recursively, always choosing the split that minimizes the sum of squares error.<\/p>\n The greedy approach is not guaranteed to find the global optimum, but it is often very effective in practice. It is also relatively simple to implement, which makes it a popular choice for decision tree learning.<\/p>\n Option A is incorrect because it does not mention the greedy approach. Option C is incorrect because it does not mention the smallest partition scheme. Option D is incorrect because it does not mention the best approximation.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”identify the best partition of the input space and response per partition to minimise sum of squares error” option2=”identify the best approximation of the above by the greedy approach (to identifying the partitions)” option3=”identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme” option4=”identify the … <\/p>\n