◑ applying · kind concept · level 3 · 26.17h
- Requiere: Statistics
Encoding, interactions, aggregations, and domain transforms — turning raw data into signal a model can use. Still the highest-leverage lever on tabular problems.
In supervised machine learning and statistical modeling, feature engineering is a preprocessing step which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant information, feature engineering significantly enhances their predictive accuracy and decision-making capability.
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- Requiere: Statistics