From 8997b617cca035daac675368c00a119b90a80d2d Mon Sep 17 00:00:00 2001 From: hadley Date: Wed, 20 Jul 2016 16:47:55 -0500 Subject: [PATCH] Note about supervised vs. unsupervised --- model.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model.Rmd b/model.Rmd index 2111e52..af1fb7f 100644 --- a/model.Rmd +++ b/model.Rmd @@ -8,7 +8,7 @@ Now that you are equipped with powerful programming tools we can finally return knitr::include_graphics("diagrams/data-science-model.png") ``` -The goal of a model is to provide a simple low-dimensional summary of a dataset. Ideally, the model will capture true "signals" (i.e. patterns generated by the phenomenon of interest), and ignore "noise" (i.e. random variation that you're not interested in). Here we only cover "predictive" models, which, as the name suggests, generate predictions. There is another type of model that we're not going to discuss: "data discovery" models. These models don't make predictions, but instead help you discover interesting relationships within your data. +The goal of a model is to provide a simple low-dimensional summary of a dataset. Ideally, the model will capture true "signals" (i.e. patterns generated by the phenomenon of interest), and ignore "noise" (i.e. random variation that you're not interested in). Here we only cover "predictive" models, which, as the name suggests, generate predictions. There is another type of model that we're not going to discuss: "data discovery" models. These models don't make predictions, but instead help you discover interesting relationships within your data. (These two categories of models are sometimes called supervised and unsuperivsed, but I don't think that terminology is particularly illuminating.) This book is not going to give you a deep understanding of the mathematical theory that underlies models. It will, however, build your intution about how statisitcal models work, and give you a family of useful tools that allow you to use models to better understand your data: