Update Scratch.Rmd vignette

vignettes/articles/Scratch.Rmd

@@ -44,11 +44,11 @@ The main hyper-parameter is the number of subclasses. We'll name our function `m
 
 Before proceeding, it helps to to review how `parsnip` categorizes models:
 
-* The model _type_ is related to the structural aspect of the model. For example, the model type `linear_reg` represents linear models (slopes and intercepts) that model a numeric outcome. Other model types in the package are `nearest_neighbors`, `decision_tree`, and so on. 
+* The model _type_ is related to the structural aspect of the model. For example, the model type `linear_reg` represents linear models (slopes and intercepts) that model a numeric outcome. Other model types in the package are `nearest_neighbor`, `decision_tree`, and so on. 
 
 * Within a model type is the _mode_. This relates to the modeling goal. Currently the two modes in the package are "regression" and "classification". Some models have methods for both models (e.g. nearest neighbors) while others are specific to a single mode (e.g. logistic regression). 
 
-* The computation _engine_ is a combination of the estimation method and the implementation. For example, for linear regression, one model is `"lm"` and this uses ordinal least squares analysis using the `lm` package. Another engine is `"stan"` which uses the Stan infrastructure to estimate parameters using Bayes rule. 
+* The computation _engine_ is a combination of the estimation method and the implementation. For example, for linear regression, one model is `"lm"` and this uses ordinary least squares analysis using the `lm` package. Another engine is `"stan"` which uses the Stan infrastructure to estimate parameters using Bayes rule. 
 
 When adding a model into `parsnip`, the user has to specific which modes and engines are used. The package also enables users to add a new mode or engine to an existing model. 
 
@@ -87,7 +87,7 @@ The next step would be the declare the main arguments to the model. These are de
 
  * A function reference for a _constructor_ that will be used to generate tuning parameter values. This should be a character vector that has a named element called `fun` that is the constructor function. There is an optional element `pkg` that can be used to call the function using its namespace. If referencing functions from the `dials` package, quantitative parameters can have additional arguments in the list for `trans` and `range` while qualitative parameters can pass `values` via this list.  
 
- * A logical value for wether the argument can be used to generate multiple predictions for a single R object. For example, for boosted trees, if a model is fit with 10 boosting iterations, many modeling packages allow the model object to make predictions for any iterations less than the one used to fit the model. In general this is not the case so one would use `has_submodels = FALSE`. 
+ * A logical value for whether the argument can be used to generate multiple predictions for a single R object. For example, for boosted trees, if a model is fit with 10 boosting iterations, many modeling packages allow the model object to make predictions for any iterations less than the one used to fit the model. In general this is not the case so one would use `has_submodels = FALSE`. 
 
 For `mda::mda()`, the main tuning parameter is `subclasses` which we will rewrite as `sub_classes`.