One of the key benefits of the logistic regression algorithm is that it is highly interpretable. This means that the outcome of the model can be interpreted as a function of the input variables. This allows us to understand how each variable contributes to the eventual outcome of the model.
In the first section, we understood that the logistic regression model consists of coefficients for each variable and an intercept that can be used to explain how the model works. In order to extract the coefficients for each variable in the model, we use the following code:
#Printing out the coefficients of each variable
print(logistic_regression.coef_)
This results in an output as illustrated by the following screenshot:
The coefficients are in the order in which the variables were in the dataset that was input into the model. In order to extract...
