Supervised Learning - Gaussian Processes, Measuring Model Performance, Reproducibility and Data Handling

Understand the concept of Gaussian Processes and their application in supervised learning.

Identify various methods for measuring model performance beyond accuracy.

Explore techniques for reproducible machine learning and data handling.

Discuss the role of hyperparameters in Gaussian Processes and how they influence model predictions.

Evaluate metrics such as precision, recall, F1 score, and AUC-ROC for a more comprehensive assessment of model performance.

Implement version control for datasets and model configurations to enhance reproducibility in experiments. By maintaining consistent data pre-processing steps and utilizing tools like Docker or Git, we can ensure that our experiments yield consistent results across different environments. Furthermore, it is essential to document all steps taken in the modelling process, including the choice of hyperparameters and the rationale behind them, to facilitate understanding and replication of the results. This level of transparency not only aids in reproducibility but also fosters collaboration among researchers and practitioners in the field.