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.
Deze oefening maakt deel uit van de cursus
Introduction to Regression with statsmodels in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Update prediction data with log_odds_ratio
____
# Print the head
print(____)