Linear Regression Review Topics

3. Linear Regression Review Topics#

Core Concepts:

  • Introduction to models and their purpose

  • Simple linear regression fundamentals

  • Multiple linear regression extensions

  • Linear regression assumptions

  • Correlation

  • Bias-variance tradeoff, over- and under-fitting

Technical Details:

  • Regression definition

  • Features and targets

  • Model fitting

    • train-test-split

    • parameters, hyper-parameters

    • predictions

    • residuals

  • Cost functions (MSE, MAE)

  • Model assessment (R², residuals)

  • Linear regression assumptions

Advanced Topics:

  • Regularization methods (why?)

    • Ridge

    • Lasso

    • Elastic Net

    • What is alpha and what does it do?

  • Feature scaling

  • Cross-validation and grid search

  • Polynomial regression

Practical Aspects:

  • Complete modeling workflow

  • Model validation strategies

  • Common pitfalls and solutions

  • Key takeaways and best practices