From 012de260d5b5d45ec490554596060f19731dd46d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= Date: Tue, 21 Mar 2023 08:17:57 -1000 Subject: [PATCH] Update README.md Update a few illustrations --- 2-Regression/4-Logistic/README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 1c39e9a69..b44e380e5 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -1,7 +1,7 @@ # Logistic regression to predict categories -![Logistic vs. linear regression infographic](./images/logistic-linear.png) -> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) +![Logistic vs. linear regression infographic](./images/linear-vs-logistic.png) + ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/) > ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb) @@ -47,8 +47,7 @@ There are other types of logistic regression, including multinomial and ordinal: - **Multinomial**, which involves having more than one category - "Orange, White, and Striped". - **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). -![Multinomial vs ordinal regression](./images/multinomial-ordinal.png) -> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) +![Multinomial vs ordinal regression](./images/multinomial-vs-ordinal.png) ### It's still linear