Problem 1:- Use the following learning schemes to analyze the zoo data (in zoo.arff): -Decision stump - weka.classifiers.DecisionStump -OneR -...

Problem 1:- Use the following learning schemes to analyze the zoo data

(in zoo.arff):

-Decision stump - weka.classifiers.DecisionStump

-OneR - weka.classifiers.OneR

-Decision table - weka.classifiers.DecisionTable -R

-C4.5 - the J48 classifier

-PART - under "rules"

How do the classifiers determine whether an animal is a mammal, bird,

reptile, fish, amphibian, insect, or invertebrate? Do the decisions made by

the classifiers make sense to you? What can you say about the accuracy of

these classifiers when classifying an animal that has not been used for

training? Why does OneR perform so badly?

Use the following learning schemes to analyze the bolts data (bolts.arff without the TIME attribute):

i. Decision Stump

ii. Decision Table (use -R)

iii. Linear Regression

iv. M5'

The dataset describes the time needed by a machine to produce and count 20 bolts. (More details can be found in the file containing the dataset.) Analyze the data. What adjustments have the greatest effect on the time to count 20 bolts? According to each classifier, how would you adjust the machine to get the shortest time to count 20 bolts?

3] Write brief report that records how your investigations proceeded and what results you found. Do not describe how to use the workbench or how the schemes in it work.