Use the WEKA
tool
•
Convert the dataset provided above (i.e., Days 1-25) into the ARFF format
(supported by WEKA), and save it in the “play_tennis.arff” file.
• For
the three future days (i.e., Days 26-28), set the values on the PlayTennis
attribute by the predictions (i.e., computed manually in Part I, by the Naïve
Bayes classification approach).
Convert
the data of these three days (i.e., Days 26-28) into the ARFF format, and save
it in the “play_tennis_test.arff” file.
•
Launch the WEKA tool, and then activate the “Explorer” environment.
•
Open the “play_tennis” dataset (i.e., saved in the “play_tennis.arff” file).
- For
each attribute and for each of its possible values, how many instances in each
class
have
the feature value (i.e., the class distribution of the feature values)?
• Go
to the “Classify” tab. Select the NaiveBayes classifier. Choose
“Percentage split” (66% for training) test mode. Run the classifier and observe
the results shown in the “Classifier output” window.
- How
many instances used for the training? How many for the test?
- How
many instances are incorrectly classified?
-
What is the MAE (mean absolute error) made by the classifier?
-
What can you infer from the information shown in the Confusion Matrix?
-
Visualize the classifier errors. In the plot, how can you differentiate between
the correctly and incorrectly classified instances? In the plot, how can you
see the detailed information of an incorrectly classified instance?
- How
can you save the learned classifier to a file?
•
Now, let’s use a separate test dataset. In the “Test options” panel select the
“Supplied test set” option. Activate the nearby “Set...” button and locate the
“play_tennis_test.arff” file.
Run
the classifier and observe the results shown in the “Classifier output” window.
- How
many instances used for the training? How many for the test?
- How
many instances are incorrectly classified?
-
What is the MAE (mean absolute error) made by the classifier?
-
What can you infer from the information shown in the Confusion Matrix?
-
Compare the test results with those observed in the previous experiment (i.e.,
using the
splitting test mode).