From 8e24cefbf24ea5d0411e01bee55d03e9caea80d6 Mon Sep 17 00:00:00 2001 From: Ossama Sybesma Date: Sat, 6 Jan 2024 19:50:35 +0100 Subject: [PATCH 1/2] fix: Typo in logistic.Rmd MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Changed Fist to First in the sentence: "Fist, we’ll use the predict() function to obtain ^η(x) for this observation." --- logistic.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logistic.Rmd b/logistic.Rmd index bb2826d..13c946a 100644 --- a/logistic.Rmd +++ b/logistic.Rmd @@ -702,7 +702,7 @@ new_obs = data.frame( ) ``` -Fist, we'll use the `predict()` function to obtain $\hat{\eta}({\bf x})$ for this observation. +First, we'll use the `predict()` function to obtain $\hat{\eta}({\bf x})$ for this observation. ```{r} eta_hat = predict(chd_mod_selected, new_obs, se.fit = TRUE, type = "link") From b99ebcc9be6f5712bedfecebf80b1d2741bb1051 Mon Sep 17 00:00:00 2001 From: Ossama Sybesma Date: Sun, 7 Jan 2024 14:51:07 +0100 Subject: [PATCH 2/2] chore: Fixed another typo in logistic.Rmd "proportion of correction classifications" to "proportion of correct classifications" --- logistic.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logistic.Rmd b/logistic.Rmd index 13c946a..148f645 100644 --- a/logistic.Rmd +++ b/logistic.Rmd @@ -909,7 +909,7 @@ In reality, we didn't actually suppress it, but instead changed `maxit` to `75`, ### Evaluating Classifiers -The metric we'll be most interested in for evaluating the overall performance of a classifier is the **misclassification rate**. (Sometimes, instead accuracy is reported, which is instead the proportion of correction classifications, so both metrics serve the same purpose.) +The metric we'll be most interested in for evaluating the overall performance of a classifier is the **misclassification rate**. (Sometimes, instead accuracy is reported, which is instead the proportion of correct classifications, so both metrics serve the same purpose.) $$ \text{Misclass}(\hat{C}, \text{Data}) = \frac{1}{n}\sum_{i = 1}^{n}I(y_i \neq \hat{C}({\bf x_i}))