Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
In-depth explanation
Feature 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
Related terms
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
Hyperparameter
Configuration settings set before training that control the learning process, not learned from data.
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