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 - weka.classifiers.j48.J48
PART - weka.classifiers.j48.PART
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?
2. Use the following learning schemes to analyze the bolts data ( bolts.arff without the TIME attribute):
Decision stump - weka.classifiers.DecisionStump
Decision table - weka.classifiers.DecisionTable -R
Linear regression - weka.classifiers.LinearRegression
M5' - weka.classifiers.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 classif