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

Manually merge 

Fixes #430
This commit is contained in:
hadley 2016-10-03 08:39:39 -05:00
parent 5aa30a729c
commit 2e65041f31
1 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
model-basics.Rmd
View File

@ -705,11 +705,11 @@ This chapter has focussed exclusively on the class of linear models, which assum
* __Generalised additive models__, e.g. `mgcv::gam()`, extend generalised * __Generalised additive models__, e.g. `mgcv::gam()`, extend generalised
linear models to incorporate arbitrary smooth functions. That means you can linear models to incorporate arbitrary smooth functions. That means you can
write a formula like `y ~ s(x)` which becomes an equation like write a formula like `y ~ s(x)` which becomes an equation like
`y = f(x)` and the `gam()` estimate what that function is (subject to some `y = f(x)` and let `gam()` estimate what that function is (subject to some
smoothness constraints to make the problem tractable). smoothness constraints to make the problem tractable).
* __Penalised linear models__, e.g. `glmnet::glmnet()`, add a penalty term to * __Penalised linear models__, e.g. `glmnet::glmnet()`, add a penalty term to
the distance which penalises complex models (as defined by the distance the distance that penalises complex models (as defined by the distance
between the parameter vector and the origin). This tends to make between the parameter vector and the origin). This tends to make
models that generalise better to new datasets from the same population. models that generalise better to new datasets from the same population.
@ -718,8 +718,8 @@ This chapter has focussed exclusively on the class of linear models, which assum
of outliers, at the cost of being not quite as good when there are no of outliers, at the cost of being not quite as good when there are no
outliers. outliers.
* __Trees__, e.g. `rpart::rpart()`, attack the problem in a complete different * __Trees__, e.g. `rpart::rpart()`, attack the problem in a completely different
way to linear models. They fit a piece-wise constant model, splitting the way than linear models. They fit a piece-wise constant model, splitting the
data into progressively smaller and smaller pieces. Trees aren't terribly data into progressively smaller and smaller pieces. Trees aren't terribly
effective by themselves, but they are very powerful when used in aggregate effective by themselves, but they are very powerful when used in aggregate
by models like __random forests__ (e.g. `randomForest::randomForest()`) or by models like __random forests__ (e.g. `randomForest::randomForest()`) or