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  3. Introduction to Regression with statsmodels in Python

Exercise

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.

Instructions 1/2

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  • Update prediction_data to add a log_odds_ratio column derived from odds_ratio.
  • Print the first five lines of prediction_data.