course
/
r4ds
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Tweak model outline

This commit is contained in:
hadley 2016-07-24 14:48:23 -05:00
parent 5742596a5e
commit fecd77847a
1 changed files with 10 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
model.Rmd
View File

@ -12,10 +12,10 @@ The goal of a model is to provide a simple low-dimensional summary of a dataset.
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:
* In [model basics], you'll learn how models work, focussing on the important
family of linear models. You'll learn general tools for gaining insight
into what a predictive model tells you about your data, focussing on simple
simulated datasets.
* In [model basics], you'll learn how models work mechanistically, focussing on
the important family of linear models. You'll learn general tools for gaining
insight into what a predictive model tells you about your data, focussing on
simple simulated datasets.
* In [model building], you'll learn how to use models to pull out known
patterns in real data. Once you have recognised an important pattern
@ -26,11 +26,12 @@ This book is not going to give you a deep understanding of the mathematical theo
understand complex datasets. This is a powerful technique, but to access
it you'll need to combine modelling and programming tools.
* In [model assessment], you'll learn a little a bit about how you might
quantitatively assess whether a model is good or not. You'll learn two
powerful techniques, cross-validation and bootstrapping, that are built
on the idea of generating many random datasets which you fit many
models to.
* In [model assessment], you'll learn more about the statistical side of
modelling. Ideally, you don't just want a model that works just with the
data that you've observe, but also generalises to new situations. You'll
learn two powerful techniques, cross-validation and bootstrapping, built
on the powerful idea of random resamples. These will help you understand
how your model will behave on new datasets.
In this book, we are going to use models as a tool for exploration, completing the trifacta of EDA tools introduced in Part 1. This is not how models are usually taught, but they make for a particularly useful tool in this context. Every exploratory analysis will involve some transformation, modelling, and visualisation.