readme edit

Author: ercbk

README.Rmd

@@ -4,13 +4,13 @@ output: github_document
 
 # Nested Cross-Validation: Comparing Methods and Implementations  
 
-Nested cross-validation has become a recommended technique for situations in which the size of our dataset is insufficient to handle both hyperparameter tuning and algorithm comparison. Using standard methods such as k-fold cross-validation in such situations results in  significant increases in optimization bias. Nested cross-validation has been shown to produce low bias in out-of-sample error estimates even using datasets with only a few hundred rows.  
+Nested cross-validation has become a recommended technique for situations in which the size of our dataset is insufficient to simultaneously handle hyperparameter tuning and algorithm comparison. Using standard methods such as k-fold cross-validation in such situations results in  significant increases in optimization bias. Nested cross-validation has been shown to produce low bias, out-of-sample error estimates even using datasets with only a few hundred rows and therefore gives a better judgemnet of generalization performance.  
 
 The primary issue with this technique is that it is computationally very expensive with potentially tens of 1000s of models being trained during the process. While researching this technique, I found two methods of performing nested cross-validation — one authored by [Sabastian Raschka](https://github.com/rasbt/stat479-machine-learning-fs19/blob/master/11_eval4-algo/code/11-eval4-algo__nested-cv_verbose1.ipynb) and the other by [Max Kuhn and Kjell Johnson](https://tidymodels.github.io/rsample/articles/Applications/Nested_Resampling.html).  
 This experiment seeks to answer two questions:  
 
 1. What's the fastest implementation of each method?  
-2. How many *repeats*, given the size of the training set, should we expect to need to obtain a reasonably accurate out-of-sample error estimate?  
+2. How many repeats, given the size of this dataset, should we expect to need to obtain a reasonably accurate out-of-sample error estimate?  
 
 With regards to the question of speed, I'll will be testing implementations of both methods from various packages which include {tune}, {mlr3}, {h2o}, and {sklearn}.  
 

README.md

@@ -2,12 +2,13 @@
 # Nested Cross-Validation: Comparing Methods and Implementations
 
 Nested cross-validation has become a recommended technique for
-situations in which the size of our dataset is insufficient to handle
-both hyperparameter tuning and algorithm comparison. Using standard
-methods such as k-fold cross-validation in such situations results in
-significant increases in optimization bias. Nested cross-validation has
-been shown to produce low bias in out-of-sample error estimates even
-using datasets with only a few hundred rows.
+situations in which the size of our dataset is insufficient to
+simultaneously handle hyperparameter tuning and algorithm comparison.
+Using standard methods such as k-fold cross-validation in such
+situations results in significant increases in optimization bias. Nested
+cross-validation has been shown to produce low bias, out-of-sample error
+estimates even using datasets with only a few hundred rows and therefore
+gives a better judgemnet of generalization performance.
 
 The primary issue with this technique is that it is computationally very
 expensive with potentially tens of 1000s of models being trained during
@@ -19,8 +20,8 @@ Johnson](https://tidymodels.github.io/rsample/articles/Applications/Nested_Resam
 This experiment seeks to answer two questions:
 
 1.  What’s the fastest implementation of each method?  
-2.  How many *repeats*, given the size of the training set, should we
-    expect to need to obtain a reasonably accurate out-of-sample error
+2.  How many repeats, given the size of this dataset, should we expect
+    to need to obtain a reasonably accurate out-of-sample error
     estimate?
 
 With regards to the question of speed, I’ll will be testing