From af0b33fe2041dd47a16404b4abdadf89c3e332bd Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Fri, 26 Apr 2024 16:51:03 +0200 Subject: [PATCH] Apply suggestions from code review Co-authored-by: Guillaume Lemaitre --- python_scripts/cross_validation_grouping.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python_scripts/cross_validation_grouping.py b/python_scripts/cross_validation_grouping.py index 3a95a825b..45b1b34e6 100644 --- a/python_scripts/cross_validation_grouping.py +++ b/python_scripts/cross_validation_grouping.py @@ -85,7 +85,7 @@ _ = plt.title("Distribution of the test scores") # %% [markdown] -# Shuffling the data results in higher cross-validated test accuracy with less +# Shuffling the data results in a higher cross-validated test accuracy with less # variance compared to when the data is not shuffled. It means that some # specific fold leads to a low score in this case. @@ -95,7 +95,7 @@ # %% [markdown] # Thus, shuffling the data breaks the underlying structure and thus makes the # classification task easier to our model. To get a better understanding, we can -# read the dataset description: +# read the dataset description in more detail: # %% print(digits.DESCR) @@ -179,7 +179,7 @@ # %% [markdown] # Once we group the digits by writer, we can incorporate this information into # the cross-validation process by using group-aware variations of the strategies -# we have explored in this course, for example, the class `GroupKFold`. +# we have explored in this course, for example, the `GroupKFold` strategy. # %% from sklearn.model_selection import GroupKFold