From bcd8a84bbe851a63c497ea3e53ea88a5c41e4cd2 Mon Sep 17 00:00:00 2001 From: Brandon Greenwell Date: Fri, 22 Jul 2016 12:24:59 -0400 Subject: [PATCH] Fixed typo (#145) --- model-assess.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model-assess.Rmd b/model-assess.Rmd index a4adbd1..40ab21e 100644 --- a/model-assess.Rmd +++ b/model-assess.Rmd @@ -38,7 +38,7 @@ There are two main resampling techniques that we're going to cover. the data to the training set, and evaluate it on the test set. This avoids intrinsic bias of using the same data to both fit the model and assess it's quality. However it introduces a new bias: you're not using all the data to - fit the model so it's going to be quite as good as it could be. + fit the model so it's not going to be quite as good as it could be. * We will use __boostrapping__ to understand how stable (or how variable) the model is. If you sample data from the same population multiple times,