Update 12-tuning-parameters.Rmd

12-tuning-parameters.Rmd

@@ -31,7 +31,7 @@ load("RData/search_examples.RData")
 
 # Model Tuning and the Dangers of Overfitting {#tuning}
 
-In order to use a model for prediction, the parameters for that model must be estimated. Some of these parameters can be estimated directly from the training data, but other parameters, called _tuning parameters_ or _hyperparameters_, must be specified ahead of time and can't be directly found from training data. These are unknown structural or other kind of values that have significant impact on the model but cannot be directly estimated from the data. This chapter will provide examples of tuning parameters and show how we use tidymodels functions to create and handle tuning parameters. We'll also demonstrate how poor choices of these values lead to overfitting and introduce several tactics for finding optimal tuning parameters values. Chapters \@ref(grid-search) and \@ref(iterative-search) go into more detail on specific optimization methods for tuning.
+In order to use a model for prediction, the parameters for that model must be estimated. Some of these parameters can be estimated directly from the training data, but other parameters, called _tuning parameters_ or _hyperparameters_, must be specified ahead of time and can't be directly found from training data. These are unknown structural or other kind of values that have significant impact on the model but cannot be directly estimated from the data. This chapter will provide examples of tuning parameters and show how we use tidymodels functions to create and handle tuning parameters. We'll also demonstrate how poor choices of these values lead to overfitting and introduce several tactics for finding optimal tuning parameter values. Chapters \@ref(grid-search) and \@ref(iterative-search) go into more detail on specific optimization methods for tuning.
 
 ## Model Parameters