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

1. 1Collect and preprocess a dataset of HR data
2. 2Apply linear regression and random forest models to predict salaries
3. 3Evaluate model performance using residual diagnostics and R²
4. 4Compare the results of both models to determine which one performs better
5. 5Consider governance and interpretability when selecting a model

Data scientists and machine learning engineers can benefit from this case study to improve their model selection and evaluation skills, and understand the importance of governance in model development

💡 Model governance and interpretability are crucial factors in model selection, beyond just performance metrics like R²

💡 Linear regression can beat random forest in predicting UK salaries! Governance matters more than R² #MachineLearning #ModelSelection