12.1 Supervised vs. unsupervised learning (1)
- Supervised vs. unsupervised machine learning (James et al. 2013, 1; Athey and Imbens 2019, 689)
- Unsupervised statistical learning: There are inputs but no supervising output; we can still learn about relationships and structure from such data
- Only observe \(X_{i}\) and try to group them into clusters
- Supervised statistical learning: involves building a statistical model for predicting, or estimating, an output based on one or more inputs
- We observe both features \(x_{i}\) and the outcome \(y_{i}\)
- Use the model to predict unobserved outcomes \((y_{i})\) of units \(i\) where we only have \(x_{i}\)
- Unsupervised statistical learning: There are inputs but no supervising output; we can still learn about relationships and structure from such data
- Good analogy: Child in Kindergarden sorts toys (with or without teacher’s input)
- Q: Would you classify topic models supervised or unsupervised ML and why?
References
Athey, Susan, and Guido W Imbens. 2019. “Machine Learning Methods That Economists Should Know About.” Annu. Rev. Econom. 11 (1): 685–725.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Springer.