代做C1 628 137、代写R编程设计、代写dataset留学生、代做R编程语言

日期：2019-03-05 09:08

Assignment 5

Due: 3/6

Note: Show all your work.

Problem 1 (10 points) Consider the following confusion matrix.

predicted class

actual class

C1 C2

C1 628 137

C2 59 394

Note: 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 the

same 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 same

training and test partitions were used for both M1 and M2) are given in the table

below. Determine whether there is a significant difference between the two models

using the statistical method discussed in Section 6 of the online lecture Module 4 (also

in Section 8.5.5, pp 372-373 of the textbook). Use a significance level of 1%. If there

is a significant difference, which one is better?

Iteration M1 M2

1 0.21 0.13

2 0.12 0.1

3 0.09 0.20

4 0.15 0.2

5 0.03 0.15

6 0.07 0.05

7 0.13 0.14

8 0.14 0.21

9 0.05 0.23

10 0.14 0.17

Note: When you calculate var(M1 – M2), calculate a sample variance (not a

population variance).

Problem 3 (20 points). For this problem, you are required to run, on Weka, Native

Bayes, J48, SimpleLogistic, RandomForest, neural network (Multilayer Perceptron),

and One R classification algorithms on german-bank.arff dataset and compare the

performance 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 longer

time than other algorithms.