Glossary · Machine Learning

What is Feature Engineering?

Creating, transforming and selecting input variables so a model can learn patterns more easily.

In more detail

Includes encoding categories, scaling numbers, handling missing values and creating new features from domain knowledge. Often improves results more than changing algorithms.

Example

Turning manufacturing year into car age, and km driven into km per year.

Learn it properlyFeature Engineering and Data Preparation for Machine Learning12 min lesson · Machine Learning

Related terms

Full glossary

Cross-ValidationEvaluating a model by training and validating it several times on different splits of the data and averaging the results.Data LeakageWhen information unavailable at prediction time sneaks into training, making a model look better than it really is.Linear RegressionA model that predicts a number as a weighted sum of input features — a best-fit straight line or plane.Precision and RecallTwo classification metrics: precision is how many flagged items were correct; recall is how many actual positives were caught.Random ForestAn ensemble model that combines many decision trees trained on random samples of data and features.Train-Test SplitDividing data into a part for training a model and a held-out part for checking how it performs on unseen data.