AI GlossaryFeature EngineeringENTRY — Machine Learning
[Machine Learning]
Feature Engineering.
The process of using domain knowledge to create new features that improve model performance.
In-depth explanation
01Feature engineering transforms raw data into features that better represent the underlying problem. This includes creating interaction features, binning continuous variables, encoding categories, extracting date features, and more. Good feature engineering often matters more than algorithm choice and requires understanding both the data and the problem domain.
Examples
02EX. 01
Creating age groups from age
EX. 02
Extracting day of week from dates
EX. 03
TF-IDF from text
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03Ensemble LearningCombining multiple models to produce better predictions than any single model.04FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.05Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.06HyperparameterConfiguration settings set before training that control the learning process, not learned from data.
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