Feature engineering is the work of reshaping raw columns into inputs that expose structure the model can actually use. A raw timestamp is nearly useless; hour of day, day of week, and days since last order are not.
On tabular data the effect is large, usually larger than swapping model families. A gradient-boosted tree with well-built ratios and aggregates beats a heavily tuned network fed raw columns. Most real gains on structured problems come from the features.
The exception is domains where the model learns its own representation. For images, audio, and text, deep networks build features internally, so hand-crafted ones add little.
The cost is time and maintenance. Every engineered feature is code that must run identically in training and in serving, or the model sees something different once it is live.
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