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 ...

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