Justify your answer - by Ben Recht - arg min

This is a live blog of Lecture 4 of the 2025 edition of my graduate machine learning class “Patterns, Predictions, and Actions.” A Table of Contents is here. The simplest form of machine learning evaluation is binary prediction. Does a patient have a disease or not? Is there a star in this region of the sky or not? Is this transaction fraud or not? We label positive outcomes as 1 and negative outcomes as 0, with the definition of positive and negative left up to the application. There are four outcomes of an individual prediction that we can assemble, each with some pre-enumerated cost. There is the cost of errors, when either you predict 0 when the outcome is 1 or predict 1 when the outcome is 0. There may also be a cost associated with a correct prediction. These costs can be negative, so you could consider receiving something for making correct predictions. I’ll write this as c(data, prediction, outcome) throughout. The first argument of the cost is the observed data, the second the

This is a live blog of Lecture 4 of the 2025 edition of my graduate machine learning class “Patterns, Predictions, and Actions.” A Table of Contents is here. The simplest form of machine learning evaluation is binary prediction. Does a patient have a disease or not? Is there a star in this region of the sky or not? Is this transaction fraud or not? We label positive outcomes as 1 and negative outcomes as 0, with the definition of positive and negative left up to the application. There are four outcomes of an individual prediction that we can assemble, each with some pre-enumerated cost. There