Hello,
Under introduction to Machine Learning on Cross Validation,
the Learn Page states the followings;
In the following code block, we display the results of varying k from 3 to 23 . For each k value, we calculate and display the average RMSE value across all of the folds and the standard deviation of the RMSE values. Across the many different k values, it seems like the average RMSE value is around 128 . You’ll notice that the standard deviation of the RMSE increases from approximately 1.1 to 37.3 as we increase the number of folds.
but the output is;
3 folds: avg RMSE: 127.19146799819767 std RMSE: 7.80114274447321
5 folds: avg RMSE: 130.57004998596955 std RMSE: 15.968993082617418
7 folds: avg RMSE: 124.74000565490935 std RMSE: 23.009326104623764
9 folds: avg RMSE: 133.85427296864364 std RMSE: 20.275996691809862
10 folds: avg RMSE: 134.50358073016668 std RMSE: 30.83892745302988
11 folds: avg RMSE: 129.58548991863123 std RMSE: 22.39316430178567
13 folds: avg RMSE: 133.05101345639838 std RMSE: 27.88932598342725
15 folds: avg RMSE: 124.86715246014936 std RMSE: 37.03384132069149
17 folds: avg RMSE: 131.3786960290144 std RMSE: 40.043451719093724
19 folds: avg RMSE: 129.0143524209374 std RMSE: 44.3383982741942
21 folds: avg RMSE: 125.49498964946545 std RMSE: 41.03033829748872
23 folds: avg RMSE: 125.27939162120605 std RMSE: 41.668089858618046
The lowest avg RMSE is circa 124 while the RMSE STD runs from 7.80 to 41.66
from the output.
Please clarify