Machine Learning Regularization and Cross-Validation for Econometrics

In this video, we explore two fundamental concepts that every econometrician needs to master when implementing machine learning methods: regularization and cross-validation. These techniques are essential for ensuring your prediction models don't overfit and generalize well to new data.

Slides used in the video are available here: https://raw.githack.com/tyleransom/structural-econ-guy/master/17-ML/04-Regularization.pdf
Source code of slides is here: https://github.com/tyleransom/structural-econ-guy/blob/master/17-ML/04-Regularization.tex

Here are the notes mentioned in the video: https://www.cs.princeton.edu/courses/archive/fall18/cos324/files/model-selection.pdf

Key Topics Covered:
- Cross-Validation
- Regularization
- L0 Regularization (Subset Selection)
- L1 Regularization (LASSO)
- L2 Regularization (Ridge Regression)
- Elastic Net: A weighted combination of L1 and L2 penalties
- High-Dimensional Applications: Both LASSO and ridge regression work even when we have more covariates than observations (high-dimensional case)

Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!

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Видео Machine Learning Regularization and Cross-Validation for Econometrics канала The Structural Econ Guy