The final assignment of this course is a competition. The challenge is to build a classification model for a given dataset, and the most accurate model wins. You are given a labeled dataset on which you can try out several algorithms, and an unlabeled dataset for which you are asked to provide the labels (predictions) using your model.
You participate in this challenge by yourself. You can ask questions to classmates, but the final process and report should be done individually. Your submitted solution will count for 1 out of 10 points on your final grade (the remaining 9 points are given based on the written exam). Since this is a competition, the best solutions will be rewarded: the solutions will be ranked on predictive accuracy, and the top 30% of submissions will gain an additional bonus point.
The labeled dataset can be found here. Use it to preprocess the data, select algorithms, optimize parameters and build models, using the WEKA Explorer or Experimenter. Note that this a rather large dataset, and some classifiers may require a lot of memory. Therefore, it is good to start WEKA with additional memory, e.g., using 'java -Xmx1000M -jar weka.jar'.
Submitting your predictions
The unlabeled dataset can be found here. When you have done all preprocessing and have selected your classifier and parameter settings, you should use the generated model to generate predictions for this unlabeled dataset. Guidelines can be found here. For instance, you can do the following:
Using this method, you will get an output that looks like this:=== Predictions on test set ===
These are the instance number, actual label (unknown), the prediction (pos or neg), the error (unknown) and the probability of each prediction. If you use WEKA 3.7, the output can be slightly different.
Finally, send in the entire prediction list.
What to hand in
You should hand in two separate, uncompressed, files.
The prediction file should start with the header line 'inst# actual predicted error probability distribution'.
Also required is a report (2-4 pages) describing the process, the classifiers tried, the pre-processing tried, and the final choices that led to your prediction.
Both files can be sent to email@example.com
Name your report xxxxxxx-assignment1.pdf or xxxxxxx-assignment2.pdf, respectively.
Put you name and student number in the report.