1) What are the effects of changing the actual values of some attributes to miss
ID: 3853228 • Letter: 1
Question
1) What are the effects of changing the actual values of some attributes to missing values when C4.5 is applied to the data
2) Compare the boosting ensemble learning method with the decision tree learning method (your answer should cover the learning approach, advantages, disadvantages)
3) You are working for a company that does consultancy work on AI. Your first task is to provide the expert advice on which machine learning technique is best suited for the scenarios below. Your answer should be backed up by an argument as to why the machine learning technique selected is best suited for that task.
Task 1: Involves around 100,000 training examples in a 4-dimensional feature space. The aim is to classify 100 test examples.
Task 2: You are going to develop a classifier to recommend which children should be assigned to special education classes in kindergarten. The classifier has to be justified to the board of education before it is implemented.
4) Which type of ensemble learning is likely to be more susceptible to noise – bagging or boosting? Explain your reasoning
5) Specify at least 2 issues that increase the complexity of incremental learning and explain why this is so.
Explanation / Answer
1) The algorithm doesn't have exact description of how to deal with the values that you change to missing values. This depends on the way you implement the algorithm. Some ways are:
Three effects with missing values in the trees are:
Please ask rest of questions in separate post.
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