FeatureEngineering

Feature engineering is the process of using domain knowledge to create new features from raw data that enable machine learning models to learn patterns more effectively. It aims to improve model performance, generalization, and data efficiency by presenting information in ways that models can exploit, capture nonlinear relationships, and reduce noise or redundancy in the input.

Common techniques include encoding categorical variables (one-hot encoding, target encoding), scaling and normalization of numerical features, extracting temporal features from dates and times (year, month, day, hour, season), and creating aggregations over groups (mean, median, count). Other approaches involve constructing interaction features (ratios, differences, products), transforming skewed distributions (log or box-cox transforms), binning continuous variables, and imputing missing values. Domain-specific features and automated methods for feature generation (such as feature engineering libraries or feature discovery tools) are also used. In text data, features may be derived from token frequencies or embeddings; in time-series data, rolling statistics and lag features are common.

The typical workflow begins with understanding the problem and data, followed by generating a set of candidate features guided by domain knowledge. Features are evaluated within a validation framework, and the feature set is refined through iteration and feature selection. It is important to guard against data leakage, overfitting, and computational costs, and to consider sensitivity to data drift. Examples include deriving year, month, and day of week from a date field; computing user-specific behavior metrics; or applying log transforms to skewed numeric inputs.

Overall, effective feature engineering often complements model selection and can be crucial for achieving strong predictive performance.