| University | The University of Auckland (TUA) |
| Subject | Machine Learning and Python Coding |
The part of the assignment is to explore several classifiers in sci-kit-learn and investigate the hyperparameter for complexity control for each of these classifiers on three datasets by setting the hyperparameter to a range of plausible values and seeing how well it does on ”held out” data. To do this you will need to train test split from sci-kit-learn. To get better estimates, simply repeat 50 times with different random splits (set the seed to get reproducible results). For simplicity, use a 50:50 train: test split in all cases. For each setting
of the hyperparameter, you then have a distribution over 50 different classification accuracies on the test set. A nice way to visualize these scores is to produce a box plot where the x-axis gives options for a parameter of the model, while the y-axis indicates the spread for classification accuracies of the classifier. Titles and Axis Labels are needed for clarity.
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