Multiple logistic regression

In a similar fashion to multiple linear regression, multiple logistic regression is about using more than one independent variable. Let's try combining the sepal length and the sepal width. Remember we need to pre-process the data a little bit:

df = iris.query("species == ('setosa', 'versicolor')") 
y_1 = pd.Categorical(df['species']).codes 
x_n = ['sepal_length', 'sepal_width'] 
x_1 = df[x_n].values

The boundary decision

Feel free to skip this section and jump to the model implementation (next section) if you are not too interested in how we can derive the boundary decision.

From the model, we have the following equation:

And from...