algorithms代写、代做dataset、C++，Python，Java程序语言调试

日期：2020-03-27 10:33

Assignment 7

Due: 3/25

Note: Show all your work.

Problem 1 (20 points). For this problem, you will run bagging and boosting algorithms

that are implemented on Weka on the processed.hungarian-2.arff dataset.

Run the following six classifier algorithms on the processed.hungarian-2.arff dataset

(1) each classifier alone, (2) Bagging with the classifier, and (3) AdaBoostM1 with the

classifier, and enter the accuracies (% correctly classified instances) in the following

table:

You also need to include in your submission screenshots of all Weka’s classification

result windows. Do Bagging and AdaBoostM1 increase accuracies?

Problem 2 (10 points) This question is about a learning classifier system XCS which

we discussed in the class. Consider the following population, which has the current set

of rules:

Suppose that a sample 1011 10 is extracted from the training dataset.

(1). Generate the match set.

(2). Determine the action from the match set.

(3). Generate the action set.

(4). Which rules are rewarded? Which rules are not rewarded?

Problem 3 (20 points). Use JMP Pro to build and test five classifier models – Na?ve

Bayes, KNN, Partition (decision tree), Boosted Tree, and Neural Network – following

the instruction in JMP-classification-assignment.pdf file.

Submission:

Include all answers in a single file and name it lastName_firstName_HW7.EXT. Here,

“EXT” is an appropriate file extension (e.g., docx or pdf). If you have multiple files,

then combine all files into a single archive file. Name the archive file as

lastName_firstName_HW7.EXT. Here, “EXT” is an appropriate archive file

extension (e.g., zip or rar). Upload the file to Blackboard.