Log odds ratio
One downside to probabilities and odds ratios for logistic regression predictions is that the prediction lines for each are curved. This makes it harder to reason about what happens to the prediction when you make a change to the explanatory variable. The logarithm of the odds ratio (the "log odds ratio" or "logit") does have a linear relationship between predicted response and explanatory variable. That means that as the explanatory variable changes, you don't see dramatic changes in the response metric - only linear changes.
Since the actual values of log odds ratio are less intuitive than (linear) odds ratio, for visualization purposes it's usually better to plot the odds ratio and apply a log transformation to the y-axis scale.
mdl_churn_vs_relationship, explanatory_data, and prediction_data are available from the previous exercise.
This exercise is part of the course
Introduction to Regression with statsmodels in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Update prediction data with log_odds_ratio
____
# Print the head
print(____)