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Chapter 11

The ML Pipeline

Curated summaries of the key literature behind this chapter — the findings, the methods, and how to put them to work.

18 referenced in this chapter

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R2P students unlock curated summaries of every key paper behind this chapter — the core findings, the methods, and how to apply them — plus resources we keep adding over time.

Using Econometrics vs. Machine Learning: What, When, and How
Joseph Simonian (2024)
Robust Estimation of a Location Parameter
Peter J. Huber (1964)
Regression Shrinkage and Selection Via the Lasso
Robert Tibshirani (1996)
Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana (2005)
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Anastasios N. Angelopoulos and Stephen Bates (2022)
plus 13 more references inside
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