代做C1 628 137、代写R编程设计、代写dataset、代做R编程语言代做Java程序|代做Processing

Assignment 5Due: 3/6Note: Show all your work.Problem 1 (10 points) Consider the following confusion matrix. predicted classactual class C1 C2C1 628 137C2 59 394Note: C1 is positive and C2 is negative.Compute sensitivity, specificity, precision, accuracy, F-meassure, and F2.Problem 2 (10 points) Suppose you built two classifier models M1 and M2 from thesame training dataset and tested them on the same test dataset using 10-fold crossvalidation.The error rates obtained over 10 iterations (in each iteration the sametraining and test partitions were used for both M1 and M2) are given in the tablebelow. Determine whether there is a significant difference between the two modelsusing the statistical method discussed in Section 6 of the online lecture Module 4 (alsoin Section 8.5.5, pp 372-373 of the textbook). Use a signific代做C1 628 137作业、代写R编程设计作业、代写dataset留学生作业、代做R编程语言作业 代做Java程序|代ance level of 1%. If thereis a significant difference, which one is better?Iteration M1 M21 0.21 0.132 0.12 0.13 0.09 0.204 0.15 0.25 0.03 0.156 0.07 0.057 0.13 0.148 0.14 0.219 0.05 0.2310 0.14 0.17Note: When you calculate var(M1 – M2), calculate a sample variance (not apopulation variance).Problem 3 (20 points). For this problem, you are required to run, on Weka, NativeBayes, J48, SimpleLogistic, RandomForest, neural network (Multilayer Perceptron),and One R classification algorithms on german-bank.arff dataset and compare theperformance of the models built by these six algorithms. Make sure that you select“Cross-validation” for “Test options.” If you have to choose one model, which one would you choose and why? Note that the neural network algorithm will take a longertime than other algorithms. 转自：http://www.3daixie.com/contents/11/3444.html