What is feature engineering?

Feature engineering aims to extract the most useful information from raw data to enable the model to learn and make accurate predictions. This process often involves domain knowledge to identify relevant data transformations or combinations. Ultimately, well-engineered features can significantly enhance model performance, sometimes more than tuning the model itself.

Common feature engineering techniques include a variety of methods used to extract or transform data into useful features. One example is normalization, which scales data into a specific range, and another is encoding categorical data using methods like one-hot encoding or label encoding. Feature creation, where new features are derived from existing ones (like combining date fields into year, month, or day), is also widely used. Other key techniques are interaction features, which capture relationships between variables, and feature selection, which removes irrelevant or redundant features. Each method aims to improve the predictive power and interpretability of the model.

Feature engineering works by transforming raw data to create more meaningful representations for machine learning algorithms. This process typically starts with understanding the problem domain and identifying which aspects of the data are important. Techniques such as scaling, encoding, or generating new features are then applied to convert raw inputs into refined features that a model can easily interpret. Once these features are created, they are used to train the model to make the algorithm more effective in recognizing patterns. The success of feature engineering is often reflected in improved model accuracy and generalization.

Examples of feature engineering include creating time-based features and binning continuous numerical data into categories. In the first, the hour or day is extracted from a timestamp to capture time-based trends in data. In the second, ages are grouped into ranges (e.g., 0-10, 11-20, etc.). Text data can be transformed into features using techniques like TF-IDF or word embeddings to represent the frequency or meaning of words. Categorical data can be transformed through one-hot encoding, converting categories into binary vectors. Feature scaling is another common example, like normalizing or standardizing numerical data to fit within a specific range.