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3 docs tagged with "methodology"

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Model Selection and Tuning

Every hyperparameter search is a way of spending a limited resource: the information in your validation set. Search too aggressively, over too many combinations, and you'll quietly overfit to the validation set itself — the exact failure the validation set was supposed to prevent in the first place.

The Debug Toolbox

Embedded debugging goes wrong in a particular way. You have a symptom — the board hangs after eleven minutes, the sensor returns zeros every hundredth read, the current draw is four times what the datasheet promises — and you reach for the instrument you are most comfortable with rather than the one that can answer the question. Then you measure the wrong thing very carefully for a day.

Train/Validation/Test Splits

The test set is spent the moment you make a decision based on it. If you tune a hyperparameter, pick a model, or even decide "let's try one more architecture" after looking at test performance, that number is no longer an honest estimate of how the model will do on truly new data. The validation set exists specifically to absorb those decisions so the test set can stay clean.