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Merge pull request #148 from OssamaSijbesma/patch-1
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chore: Fixed two typos in logistic.Rmd
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daviddalpiaz authored Jan 7, 2024
2 parents a8eda2a + b99ebcc commit e948eef
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4 changes: 2 additions & 2 deletions logistic.Rmd
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Expand Up @@ -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")
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### 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}))
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