A parking-occupancy lab used a clustering algorithm on labeled occupancy classes, guessed a learning rate, spilled the same rows into train and test, and called a two-fold split “k-fold.” Which named reviews and tests mitigate those four risks?

Clustering on labeled occupancy classes, a guessed learning rate, spilled train and test rows, and a two-fold split called “k-fold” are four named risks. Match them with an algorithm suitability review (and A/B), ML functional performance testing or A/B for hyperparameters, a data-allocation review, and ML functional performance testing of the evaluation approach. Four adversarial passes, canaries, or Chapter 5 uniqueness checks miss that map.