Online Training Workshop on Machine Learning for Economic Analysis - May 2024 - Day 2
Objectives: The objective of the workshop is to train participants in the main Machine Learning (ML) methods in the specific context of economic analysis. The training will focus on supervised and semi-supervised classification techniques. In this context, the aim will be to propose a reflection on the existence of a trade-off between the interpretability and predictive performance of models derived from ML algorithms. This intermediate-level course is aimed at participants with notions of statistical learning and basic notions of programming.
Knowledge transfer: the course will combine a general presentation of ML with a more detailed presentation of a few methods commonly used in the context of economic analysis.
Day 2 (May 21)
General introduction to ML (part 2)
Performance evaluation of ML algorithms: evaluation criteria and methodology.
Trade-off between complexity and performance.
Choosing hyperparameters by cross-validation: controlling the risk of over-fitting.
The issue of unbalanced samples.
Supervised learning methods: Bagging
Principle of Bagging methods.
Advantages and limitations of Bagging.
The case of random forest.