# 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.

It is clear that the process of parameter estimation is important, since this is how we can make a predictive model from 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...