In this section, we will walk through the implementation of logistic regression in Python within the `packtml`

package. We will start off with a brief recap of what logistic regression seeks to accomplish and then go over the source code and look at an example.

### Note

Recall that logistic regression seeks to classify a sample into a discrete category, also known as **classification**. The logistic transformation allows us to transform the log odds that we get from the inner product of our parameters and `X`

.

Notice that we have three Python files open. One is `extmath.py`

, from within the `utils`

directory inside of `packtml`

; another is `simple_logistic.py`

, from within the `regression`

library in `packtml`

; and the final one is an `example_logistic_regression.py`

file, inside the `examples`

directory and `regression`

.

We will dive right into the code base using the following steps:

- We will start with the
`extmath.py`

file. There are two functions that we will be using here...