For this recipe, we will implement logistic regression to predict the probability of low birthweight.
Logistic regression is a way to turn linear regression into a binary classification. This is accomplished by transforming the linear output in a sigmoid function that scales the output between zero and 1. The target is a zero or 1, which indicates whether or not a data point is in one class or another. Since we are predicting a number between zero or 1, the prediction is classified into class value 1''' if the prediction is above a specified cut off value and class 0
otherwise. For the purpose of this example, we will specify that cut off to be 0.5
, which will make the classification as simple as rounding the output.
The data we will use for this example will be the low birthweight data that is obtained through the University of Massachusetts Amherst statistical dataset repository (https://www.umass.edu/statdata/statdata/). We will be predicting...