start with the idea before the implementation.
the mechanisms you need to reason about.
Splits and impurity
Splits and impurity 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.
Depth and overfitting
Depth and overfitting 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.
Feature interactions
Feature interactions 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.
Pruning
Pruning 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.
train shallow and deep trees
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
visualize a tree
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
measure overfitting
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
explain a split
ask cortex to test me →identify high-variance behavior
ask cortex to test me →compare with a linear baseline
ask cortex to test me →what usually goes wrong.
unbounded depth
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
unstable feature importance
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
assuming interpretability at scale
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 Trees 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.