F3PythonAdvancedAbout 180 min to complete
Logistic Regression (Statsmodels)
Every model so far predicted a number (a tip, a body mass). But many of the most important questions are yes/no: will this customer churn?
Notebook
About 180 minutes to complete
Enrol to read the notebook and download it to run.
What this module covers
Every model so far predicted a number (a tip, a body mass). But many of the most important questions are yes/no: will this customer churn?
By the end of it
- Explain why linear regression fails for a binary outcome and how the sigmoid/logit fixes it
- Understand odds, log-odds (logit), and interpret coefficients as odds ratios via exp(β)
- Fit a logistic model with statsmodels (smf.logit) and read its summary
- Produce predicted probabilities and turn them into class predictions with a threshold
- Evaluate a classifier: confusion matrix, accuracy, precision, recall, F1, and the ROC curve / AUC
- Handle multiple and categorical predictors, and read pseudo-R² and the likelihood-ratio test
- Recognise the pitfalls: reading coefficients as probabilities, a bad threshold, and class imbalance