Estimating the Coefficients and Intercepts of Logistic Regression

In the previous chapter, we learned that the coefficients of a logistic regression model (each of which goes with a particular feature), as well as the intercept, are determined using the training data when the fit method is called on a logistic regression model in scikit-learn. 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: with just these numbers, we can use a logistic regression model in any environment where we can perform common mathematical functions.