Assumptions of Logistic Regression, Clearly Explained

Understanding and implementing the assumption checks behind one of the most important statistical techniques in data science - Logistic Regression

• Link to TowardsDataScience article: https://towardsdatascience.com/assumptions-of-logistic-regression-clearly-explained-44d85a22b290
• Logistic regression is a highly effective modeling technique that has remained a mainstay in statistics since its development in the 1940s.
• Given its popularity and utility, data practitioners should understand the fundamentals of logistic regression before using it to tackle data and business problems.
• In this project, we explore the key assumptions of logistic regression with theoretical explanations and practical Python implementation of the assumption checks.

Contents

(1) Logistic_Regression_Assumptions.ipynb

• The main notebook containing the Python implementation codes (along with explanations) on how to check for each of the 6 key assumptions in logistic regression

(2) Box-Tidwell-Test-in-R.ipynb

• Notebook containing R code for running Box-Tidwell test (to check for logit linearity assumption)

(3) /data

• Folder containing the public Titanic dataset (train set)

(4) /references

• Folder containing several sets of lecture notes explaining advanced regression

Special Thanks

• @dataninj4 for correcting imports and adding .loc referencing in diagnosis_df cell so that it runs without errors in Python 3.6/3.8
• @ArneTR for rightly pointing out that VIF calculation should include a constant, and correlation matrix should exclude target variable

References

• Machine Learning Essentials - Practical Guide in R
• Logistic and Linear Regression Assumptions - Violation Recognition and Control
• Testing linearity in the logit using Box-Tidwell Transformation in SPSS - Youtube
• Logistic Regression using SPSS
• Statistics How To - Cook's Distance
• Statsmodels Documentation - GLM
• Statsmodels Documetation - Logit Influence example notebook
• PennState Eberly College of Science - Stat 462
• Statistics Solution - Assumptions of Logistic Regression
• Course Notes for IS 6489 - Statistics and Predictive Analytics
• MSc in Big Data Analytics at Carlos III University of Madrid - Notes for Predictive Modeling
• Freakonometrics - Residuals from a Logistic Regression
• Kaggle - Titanic - Logistic Regression with Python
• Yellowbrick API Reference - Cook's Distance
• DataCamp - Understanding Logistic Regression in Python
• Statology - How to Calculate Cook's Distance
• ResearchGate - Box-Tidwell Test in SPSS
• CrossValidated - Why include x ln x interaction term helps
• UCLA IDRE - Logistic Regression Diagnostics
• Logistic and Linear Regression Assumptions: Violation Recognition and Control