Experiments:
You must run a sufficiently large and coherent set of experiments.
Start with a basic experiment with default parameters (if possible),
and design new experiments varying the settings
(i.e., pre-processing, parameters, and/or post-processing, ideally
varying one setting at a time)
based on the results that you obtain in your experiments.
Each experiment should be motivated by a previous experiment,
and by the guiding questions.
- For each experiment you ran describe:
- Objectives: Which of your 3 specific questions/conjectures
about the dataset domain you aim to answer/validate with
this experiment. Describe also any additional objectives for this
experiment that might have been motivated by your previous
experiments.
- Data: What data did you use to construct and test your model?
- Parameters and Settings:
Describe what parameter values and other settings you used
and why.
- Additional Pre or Post Processing:
Any additional pre or post processing done to the data or the
model in order to improve the model's performance,
as measured by the performance metric(s) chosen.
- Analysis of the constructed model:
- Describe the constructed model
(e.g., size of the model, readability).
If the model is readable summarize in your own words what the model
says, focusing on the most interesting/relevant patterns.
Elaborate on if and how the model answers the objectives of this
experiment.
- State what the performance of the model is, using the performance
metrics provided in the project description. If applicable,
elaborate on the confusion matrix and/or other relevant
performance indicators.
- How long it took Weka/Python to construct this model?
- Compare the performace of this model with that of other
models constructed in this project for this dataset.