Feature engineering is the technique of transforming raw data into features that better represent the problem for the machine learning algorithm. And like any other AI process: the quality of the features determines the quality of the model.
How it works
Feature engineering involves: identifying which data fields are relevant to the task, transforming categorical data into numeric, grouping values into ranges, creating new variables from others and selecting the most informative features. The goal is for the model to understand the data better, improving accuracy and generalization.
Practical example
In a sales forecasting model, the raw data includes: date, product, category, price, customer. Feature engineering can create: day of week effect (Monday is bad for sales), season (summer increases sales), price percentage above the average value, customer purchase history. These transformations help the model understand patterns that are not explicit in the data.
Related terms
- machine-learning: AI field that learns from data
- raw-data: information without processing
- feature-selection: process of choosing the most relevant features
How to test today
For testing, use a simple sales dataset, generate new variables (season data, day of week) and compare the model's accuracy with and without feature engineering.
Frequently asked questions
- How does feature engineering work? It consists of transforming data into more informative representations, with one-hot encoding, binarization, logarithmic transformations, among others.
- Is feature engineering necessary? Yes. Any machine learning model benefits from well encoded features. Raw data is often not enough to capture complex patterns.
- How to expand the feature set? Use categorical encoding, frequency encoding, date encoding, temporal feature engineering (day of week, month, season) and, for texts, TF-IDF, embeddings and paradigm representations.