From 03fc69b5fcf9f05b0574de8a927ca3787c7047f6 Mon Sep 17 00:00:00 2001
From: Carlotta Castelluccio <82521518+carlotta94c@users.noreply.github.com>
Date: Mon, 25 Sep 2023 12:06:51 +0200
Subject: [PATCH] Fixing image path

---
 2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index 38741c33..c0b39d27 100644
--- a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -20,7 +20,7 @@
                 "\n",
                 "✅ Deepen your understanding of working with this type of regression in this [Learn module](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
                 "\n",
-                "#### **Prerequisite**\n",
+                "## Prerequisite\n",
                 "\n",
                 "Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.\n",
                 "\n",
@@ -88,7 +88,7 @@
                 "\n",
                 "- **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).\n",
                 "\n",
-                "![Multinomial vs ordinal regression](./images/multinomial-vs-ordinal.png)\n",
+                "![Multinomial vs ordinal regression](../../images/multinomial-vs-ordinal.png)\n",
                 "\n",
                 "#### **Variables DO NOT have to correlate**\n",
                 "\n",