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# Test Document Essay

1244 words - 5 pages

Assignment 3 – Classification

Problem 1 (25 points) Consider the following dataset:

ID | A1 | A2 | A3 | Class |
1 | Low | Mild | East | Yes |
2 | Low | Hot | West | No |
3 | Medium | Mild | East | No |
4 | Low | Mild | East | Yes |
5 | High | Mild | East | Yes |
6 | Medium | Hot | West | No |
7 | High | Hot | West | Yes |
8 | Low | Cool | West | No |
9 | Medium | Cool | East | Yes |
10 | High | Cool | East | No |
11 | Medium | Mild | West | Yes |
12 | Medium | Cool | West | No |
13 | Medium | Hot | West | Yes |
14 | high | Hot | East | Yes |

Suppose we have a new tuple X = (A1 = Medium, A2 = Cool, A3 = East). Predict the class ...view middle of the document...

Follow the instructions below. There are three subproblems plus one extra credit problem.

(Some screenshots are small and are not easy to see in this document. You may have to zoom in from your pdf reader software.)

(1) Start Weka

(2) Open Explorer by clicking Explorer.

(3) Click Open file, browse to the location where you have weather.normial.arff file and open it.

(4) Explorer window appears as shown below.

(5) Among many other things, you can see there are 14 instances and 5 attributes in the dataset and the play attribute is the class attribute. Click Classify tab.

(6) Click Choose. Classifier selection window appears. Select NaïveBayes under Classifier – Bayes.

(7) The following screenshot shows that NaiveBayes is selected.

Accept the default test option and click Start.

(8) Classifier output is shown in the right window.

Problem 3-1. Capture this screenshot and paste it onto your submission.
Problem 3-2. At the bottom of the output window, you will see Confusion Matrix. It has four numbers. Explain the meaning of each number (confusion matrix is discussed in pp. 359 - 362).

Do not exit explorer yet.

What you did so far are two things: (1) You built a Naïve Bayes model using the weather.nominal.arff dataset and (2) You tested the performance of your model using 10-fold cross-validation (We will discuss this testing method in the next module. If you want you can read about this method in page 365).

Now let’s predict the class label of instances whose class labels are unknown. First, create a text file that contains the following:

@relation weather.test01

@attribute outlook {sunny, overcast, rainy}
@attribute temperature {hot, mild, cool}
@attribute humidity {high, normal}
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}

@data
rainy,hot,high,FALSE,yes
sunny,hot,normal,TRUE, yes
overcast,hot,high,TRUE,yes
rainy,mild,normal,FALSE,yes
rainy,mild,normal,FALSE,yes
sunny,cool,normal,TRUE, yes
overcast,hot,normal,TRUE,yes
sunny,mild,normal,FALSE, yes
sunny,cool,normal,TRUE,yes
rainy,mild,high,FALSE,yes

Name the file as weather.nb.test01.arff. Notice that the relation name was changed and that all class attribute values are set to yes (The class attribute values are irrelevant in this case because weka will predict the values. If you want you can put all no’s). You will provide this file to weka as a test dataset. Then, weka will predict the class label of each instance using the model you built. Follow the following steps.

(1) Choose Supplied test set for Test options as shown below.

(2) Click Set. The following dialogue box appears.

(3) Click Open file, browse to where...

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