Your task for the assignment will be to create generalisable deep learning models using the supplied data and TensorFlow/Keras. If you do not have, or need help installing this software on your computer, please contact the module organiser. You will be supplied with the “Cold” and “Not-Cold” labels for the training and development partitions. Using these, you can develop different systems for performing the 2-class classification task.
Then, from your most suitable models, you will then generate predictions for the test set. The accuracy of the test set predictions will be independently verified by your lecturer. You are required to verify the results of 5 different models.
Additionally, you will implement the Local Interpretable Model-agnostic Explanations (LIME) framework to help compare the performance of your developed models on exemplar data instances.
Task 1: Baseline System and Data Balancing (15% of marks)
As deep neural networks contain a considerably higher number of parameters and a wider choice of hyperparameters than other classification approaches, it is often necessary to first implement a simpler classifier to understand the difficulty of the associated task. Use the supplied data training and development data partitions for this task.
1. Generate a suitable baseline classification model. For this, use either a Support Vector Machine or Random Forest Classifier. Both are implementable through SciKit-Learn.
2. Identify the most suitable metric for assessing system performance
3. Perform a suitable grid-search to find a reasonable set-up of your chosen classifier
4. Using your chosen baseline system, explore the benefits of random downsampling of the
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