readme edits

Author: ercbk

README.Rmd

@@ -111,10 +111,10 @@ durations
 Experiment details:  
 
   * The fastest implementation of each method will be used in running a nested cross-validation with different sizes of data ranging from 100 to 5000 observations and different numbers of repeats of the outer-loop cv strategy.  
-      * The {mlr3} implementation was the fastest for Raschka's method, but the Ranger-Kuhn-Johnson implementation is close. So I'll be using Ranger-Kuhn-Johnson for both methods.  
+      * The {mlr3} implementation was the fastest for Raschka's method, but the Ranger-Kuhn-Johnson implementation was close. To simplify, I'll be using Ranger-Kuhn-Johnson for both methods.  
   * The chosen algorithm and hyperparameters will used to predict on a 100K row simulated dataset and the mean absolute error will be calculated for each combination of repeat, data size, and method.  
-  * Runtimes began to explode after n = 800 for my 8 vcpu, 16 GB RAM desktop, so I ran this experiment using AWS instances: a r5.2xlarge for the Elastic Net and a r5.24xlarge for Random Forest.  
-  * I'll be iterating through different numbers of repeats and sample sizes, so I'll be transitioning from imperative scripts to a functional approach. Given the long runtimes and impermanent nature of my internet connection, it would be nice to cache each iteration as it finishes. The [{drake}](https://github.com/ropensci/drake) package is superb on both counts, so I'm using it to orchestrate.  
+  * Runtimes began to explode after n = 800 for my 8 vcpu, 16 GB RAM desktop, therefore I ran this experiment using AWS instances: a r5.2xlarge for the Elastic Net and a r5.24xlarge for Random Forest.  
+  * I'll be transitioning from imperative scripts to a functional approach, because I'm iterating through different numbers of repeats and sample sizes. Given the long runtimes and impermanent nature of my internet connection, it would also be nice to cache each iteration as it finishes. The [{drake}](https://github.com/ropensci/drake) package is superb on both counts, so I'm using it to orchestrate.  
 
 ```{r perf_build_times, echo=FALSE, message=FALSE, cache=TRUE}
 
@@ -146,10 +146,10 @@ fill_colors <- unname(swatches::read_ase("palettes/Forest Floor.ase"))
 
 ggplot(subtargets, aes(y = elapsed, x = repeats,
                        fill = n, label = elapsed)) +
-   geom_col(position = position_dodge(width = 0.8)) +
+   geom_col(position = position_dodge(width = 0.85)) +
    scale_fill_manual(values = fill_colors[4:7]) +
    geom_text(hjust = 1.3,  size = 3.5,
-             color = "white", position = position_dodge(width = 0.8)) +
+             color = "white", position = position_dodge(width = 0.85)) +
    coord_flip() +
    labs(y = "Runtime (hrs)", x = "Repeats",
         title = "Kuhn-Johnson", fill = "Sample Size") +

README.md

@@ -91,20 +91,22 @@ Experiment details:
     100 to 5000 observations and different numbers of repeats of the
     outer-loop cv strategy.
       - The {mlr3} implementation was the fastest for Raschka’s method,
-        but the Ranger-Kuhn-Johnson implementation is close. So I’ll be
-        using Ranger-Kuhn-Johnson for both methods.  
+        but the Ranger-Kuhn-Johnson implementation was close. To
+        simplify, I’ll be using Ranger-Kuhn-Johnson for both methods.  
   - The chosen algorithm and hyperparameters will used to predict on a
     100K row simulated dataset and the mean absolute error will be
     calculated for each combination of repeat, data size, and method.  
   - Runtimes began to explode after n = 800 for my 8 vcpu, 16 GB RAM
-    desktop, so I ran this experiment using AWS instances: a r5.2xlarge
-    for the Elastic Net and a r5.24xlarge for Random Forest.  
-  - I’ll be iterating through different numbers of repeats and sample
-    sizes, so I’ll be transitioning from imperative scripts to a
-    functional approach. Given the long runtimes and impermanent nature
-    of my internet connection, it would be nice to cache each iteration
-    as it finishes. The [{drake}](https://github.com/ropensci/drake)
-    package is superb on both counts, so I’m using it to orchestrate.
+    desktop, therefore I ran this experiment using AWS instances: a
+    r5.2xlarge for the Elastic Net and a r5.24xlarge for Random
+    Forest.  
+  - I’ll be transitioning from imperative scripts to a functional
+    approach, because I’m iterating through different numbers of repeats
+    and sample sizes. Given the long runtimes and impermanent nature of
+    my internet connection, it would also be nice to cache each
+    iteration as it finishes. The
+    [{drake}](https://github.com/ropensci/drake) package is superb on
+    both counts, so I’m using it to orchestrate.
 
 ![](README_files/figure-gfm/perf_bt_charts-1.png)<!-- -->