From 8124c5bf26e4ff8ef067f3f8da603126c2d9232b Mon Sep 17 00:00:00 2001
From: Daniel Campos <12107659+daniel-m-campos@users.noreply.github.com>
Date: Mon, 19 Feb 2024 07:13:01 -0600
Subject: [PATCH] Fix formatting (#762)

---
 notebooks/metrics_classification.ipynb   | 14 +++++++-------
 python_scripts/metrics_classification.py | 12 ++++++------
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/notebooks/metrics_classification.ipynb b/notebooks/metrics_classification.ipynb
index 9191bae79..5ecbabc0f 100644
--- a/notebooks/metrics_classification.ipynb
+++ b/notebooks/metrics_classification.ipynb
@@ -311,13 +311,13 @@
     "blood when the classifier predicted so or the fraction of people predicted to\n",
     "have given blood out of the total population that actually did so.\n",
     "\n",
-    "The former metric, known as the precision, is defined as TP / (TP + FP) and\n",
+    "The former metric, known as the precision, is defined as `TP / (TP + FP)` and\n",
     "represents how likely the person actually gave blood when the classifier\n",
-    "predicted that they did. The latter, known as the recall, defined as TP / (TP\n",
-    "+ FN) and assesses how well the classifier is able to correctly identify\n",
-    "people who did give blood. We could, similarly to accuracy, manually compute\n",
-    "these values, however scikit-learn provides functions to compute these\n",
-    "statistics."
+    "predicted that they did. The latter, known as the recall, defined as\n",
+    "`TP / (TP + FN)` and assesses how well the classifier is able to correctly\n",
+    "identify people who did give blood. We could, similarly to accuracy,\n",
+    "manually compute these values, however scikit-learn provides functions to\n",
+    "compute these statistics."
    ]
   },
   {
@@ -664,4 +664,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/python_scripts/metrics_classification.py b/python_scripts/metrics_classification.py
index a620c3612..e6304c851 100644
--- a/python_scripts/metrics_classification.py
+++ b/python_scripts/metrics_classification.py
@@ -188,13 +188,13 @@
 # blood when the classifier predicted so or the fraction of people predicted to
 # have given blood out of the total population that actually did so.
 #
-# The former metric, known as the precision, is defined as TP / (TP + FP) and
+# The former metric, known as the precision, is defined as `TP / (TP + FP)` and
 # represents how likely the person actually gave blood when the classifier
-# predicted that they did. The latter, known as the recall, defined as TP / (TP
-# + FN) and assesses how well the classifier is able to correctly identify
-# people who did give blood. We could, similarly to accuracy, manually compute
-# these values, however scikit-learn provides functions to compute these
-# statistics.
+# predicted that they did. The latter, known as the recall, defined as
+# `TP / (TP + FN)` and assesses how well the classifier is able to correctly
+# identify people who did give blood. We could, similarly to accuracy,
+# manually compute these values, however scikit-learn provides functions to
+# compute these statistics.
 
 # %%
 from sklearn.metrics import precision_score, recall_score