Exercise: Obtaining Probabilities from Logistic Regression Model

Discovering predicted probabilities

How does logistic regression make predictions? Now that we’re familiar with accuracy, true and false positives and negatives, and the confusion matrix, we can explore new ways of using logistic regression to learn about more advanced binary classification metrics. So far, we’ve only considered logistic regression as a “black box” that can learn from labeled training data and then make binary predictions on new features. While we will learn about the workings of logistic regression in detail later in the course, we can begin to peek inside the black box now.

One thing to understand about how logistic regression works is that the raw predictions—in other words, the direct outputs from the mathematical equation that defines logistic regression—are not binary labels. They are actually probabilities on a scale from 0 to 1 (although, technically, the equation never allows the probabilities to be exactly equal to 0 or 1, as we’ll see later). These probabilities are only transformed into binary predictions through the use of a threshold. The threshold is the ...