Encoding, scaling and interaction terms, target and temporal leakage, train-serve skew, and the features that quietly encode the label.
3 questions in Machine Learning.
One-hot is unusable at that width, so the options are frequency encoding, smoothed target encoding, hashing, or a library's native categorical handling. Target encoding is the strongest and the most dangerous, because it must be fitted inside each fold or it leaks the label.
Target leakage is a feature carrying information that would not exist at prediction time, so the model is partly reading the answer. The two forms are a column written after the label event and an aggregate over a window including it. You catch it by auditing when each column is written.
Skew is any difference between the feature values a model trained on and the ones it receives at request time. It comes from two implementations of one transformation, joins that ignore the as-of timestamp, and features unavailable in a request. Prevent it with shared code, as-of joins and served-vector logging.