start with the idea before the implementation.
the mechanisms you need to reason about.
Linear regression
Linear regression is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Loss functions
Loss functions is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Regularization
Regularization is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Residual analysis
Residual analysis is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
fit a mean baseline and linear model
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
plot residuals
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
compare MAE and RMSE
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
identify leakage
ask cortex to test me →interpret coefficients carefully
ask cortex to test me →select a useful metric
ask cortex to test me →what usually goes wrong.
extrapolation
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
target leakage
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
ignoring heteroscedasticity
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short Regression engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.