Use latest stable version. Use both the graphical interface (Explorer) (here is a guide (pdf)) and command line interface (CLI).

**B. Use the following learning schemes**, with the default
settings to analyze the weather data (in weather.arff). For test
options, first choose "Use training set", then choose "Percentage
Split" using default 66% percentage split. Report model percent error
rate.

- ZeroR (majority class)
- OneR
- Naive Bayes Simple
- J4.8

## Answer:

ZeroRModel: Yes Evaluate using training set: 5/14 = 35% errors Evaluate using split: 2/5 = 40% errorsOneRModel: sunny -> no overcast -> yes rainy -> yes Evaluate using training set, error rate: 4/14 =29% Evaluate using split, error rate: 3/5 = 60%NaiveBayes (simple)Model: (omitted to save space) Evaluate using training set, error rate: 1/14 =7% Evaluate using split, error rate: 2/5 = 40%J48 pruned treeModel: outlook = sunny | humidity <= 75: yes (2.0) | humidity > 75: no (3.0) outlook = overcast: yes (4.0) outlook = rainy | windy = TRUE: no (2.0) | windy = FALSE: yes (3.0) Evaluate using training set, error rate: 0/14 =0% Evaluate using split, error rate: 3/5 = 60%

**C. Which of these classifiers are you more likely to trust when determining whether to play? Why?
**

**D. What can you say about accuracy when using training set data and when using a separate percentage to train? **