Structural Changes

Structural transformations modify how data is scaled, distributed, or organized without creating new information. Normalization squishes values into standard ranges like 0-1, making it fair to compare things measured in different units (like comparing dollars and temperatures). Standardization centers everything around zero with a consistent spread, which helps algorithms that assume data is roughly bell-curve shaped.

Log transformations are particularly handy for data with a long tail—like salaries, where most people earn moderate amounts but a few earn astronomical sums. Taking the log compresses these huge gaps, making patterns easier to spot. Other power transformations (square root, Box-Cox) offer different ways to tame unruly data. Not all algorithms need these adjustments—decision trees don't care much about scale, while neural networks and linear models definitely do. Good transformation choices combine statistical knowledge with practical common sense. It's the bridge between raw data and what your algorithm needs to perform its best.