From 02f664bc0ef39e4a19865f2b948257f63db92a7f Mon Sep 17 00:00:00 2001 From: Dave Buckley Date: Tue, 26 Dec 2017 20:00:52 +0000 Subject: [PATCH] fix sentence that does not read well --- book/formal.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/book/formal.tex b/book/formal.tex index 027bdb2..9ff6fc2 100644 --- a/book/formal.tex +++ b/book/formal.tex @@ -544,7 +544,7 @@ \section{Real World Applications of Machine Learning} The important thing about this sequence of steps is that \emph{in any one, things can go wrong.} That is, between any two rows of this table, we are \emph{necessarily} accumulating some additional error against our original real world goal of increasing revenue. For example, in step 5, we decided on a representation that left out many possible variables we could have logged, like time of day or season of year. By leaving out those variables, we set an explicit upper bound on how well our learned system can do. -It is often an effective strategy to run an \concept{oracle experiment}. In an oracle experiment, we assume that everything below some line can be solved perfectly, and measure how much impact that will have on a higher line. As an extreme example, before embarking on a machine learning approach to the ad display problem, we should measure something like: if our classifier were \emph{perfect}, how much more money would we make? If the number is not very high, perhaps there is some better for our time. +It is often an effective strategy to run an \concept{oracle experiment}. In an oracle experiment, we assume that everything below some line can be solved perfectly, and measure how much impact that will have on a higher line. As an extreme example, before embarking on a machine learning approach to the ad display problem, we should measure something like: if our classifier were \emph{perfect}, how much more money would we make? If the number is not very high, perhaps there is some better use of our time. Finally, although this sequence is denoted linearly, the entire process is highly interactive in practice. A large part of ``debugging'' machine learning (covered more extensively in Chapter~\ref{sec:prac} involves trying to figure out where in this sequence the biggest losses are and fixing that step. In general, it is often useful to \emph{build the stupidest thing that could possibly work}, then look at how well it's doing, and decide if and where to fix it.