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
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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