start with the idea before the implementation.
the mechanisms you need to reason about.
Probability outputs
Probability outputs 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.
Thresholds
Thresholds 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.
Class imbalance
Class imbalance 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.
Calibration
Calibration 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 a baseline classifier
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
plot precision-recall
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
choose a threshold from cost
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
explain false positives vs false negatives
ask cortex to test me →use stratified evaluation
ask cortex to test me →measure calibration
ask cortex to test me →what usually goes wrong.
accuracy-only evaluation
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
random split on time data
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
threshold 0.5 by habit
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 Classification 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.