Estimating the Coefficients and Intercepts of Logistic Regression

In the previous chapter, we learned that the coefficients of a logistic regression (each of which goes with a particular feature), and the intercept, are determined when the .fit method is called on a logistic regression model in scikit-learn using the training data. These numbers are called the parameters of the model, and the process of finding the best values for them is called parameter estimation. Once the parameters are found, the logistic regression model is essentially a finished product; therefore, with just these numbers, we can use the trained logistic regression in any environment where we can perform common mathematical functions.

It is clear that the process of parameter estimation is important, since this is how we can make a functional model using our data. So, how does parameter estimation work? To understand this, the first step is to familiarize ourselves with the concept of a cost function. A cost function...