## 7.5 Terminological differences (2)

• Well-established labels in the older literatures vs. new ML terminology
• Sample used to estimate the parameters vs. training sample
• Model is estimated vs. Model is trained
• Regressors, covariates, predictors vs. features (or inputs)
• Dependent variable/outcome vs. output
• Regression parameters (coefficients) vs. weights
• Supervised vs. unsupervised machine learning
• Good analogy: Child in Kindergarden sorts toys (with or without teacher’s input )
• 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 $$y_{i}$$
• 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

### 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.