When Linear Regression Beats Random Forest: A UK Salary Prediction Case Study

1. 1Collect and preprocess HR data with 720 rows to predict UK salaries
2. 2Implement linear regression and random forest models to compare performance
3. 3Evaluate model complexity using residual diagnostics and governance metrics
4. 4Compare R² values of both models to determine which one performs better
5. 5Apply governance principles to ensure model reliability and interpretability

Data scientists and analysts can benefit from this case study to improve their model selection and evaluation skills, especially when working with HR data

💡 Model complexity and governance are crucial factors in model selection, beyond just R² values

💡 Linear regression can beat random forest in salary prediction! Governance matters more than R² #datascience #machinelearning