## Logistic Regression

In linear regression, we modeled continuous values, such as the price of a home. In (binomial) logistic regression, we apply a logistic sigmoid function to the output, resulting in a value between 0 and 1. This value can be interpreted as the probability that the observation belongs to **class 1**. By setting a cutoff/threshold (such as *0.5*), we can use it as a classifier. This is the same approach we used with the neural networks in the previous chapter. The sigmoid function is , where is the output from the linear regression:

###### Figure 5.21: A plot of the sigmoid function

*Figure 5.21* shows the sigmoid function applied to the output . The dashed line represents our cutoff of **0.5**. If the predicted probability is above this line, the observation is predicted to be in **class 1**, otherwise, it's in **class 0**.

For logistic regression, we use the generalized version of **lm()**, called **glm()**, which can be used for multiple types of regression. As we are performing binary...