Feature engineering is a crucial step in the machine learning process. It involves creating new input features or modifying existing ones to improve model performance. In this tutorial, we'll cover the basics of feature engineering and explore some common techniques.
What is Feature Engineering?
Feature engineering is the process of using domain knowledge to extract features from raw data that make machine learning algorithms work better. This can involve creating new features, transforming existing features, or selecting the most relevant features.
Common Techniques
- Feature Creation: This involves creating new features based on the existing data. For example, you might create a new feature that represents the number of days between two dates.
- Feature Transformation: This involves transforming existing features to make them more suitable for machine learning algorithms. For example, you might convert categorical variables into numerical values using one-hot encoding.
- Feature Selection: This involves selecting the most relevant features to use in the model. This can be done using various techniques such as mutual information or recursive feature elimination.
Example
Let's say you're working on a machine learning project to predict house prices. You might start with a dataset that includes features such as the number of bedrooms, the square footage of the house, and the age of the house. To improve the performance of your model, you could:
- Create a new feature: The age of the house divided by the square footage. This could provide information about how the house has depreciated over time.
- Transform a feature: Convert the age of the house into a categorical variable (e.g., "new", "medium", "old") to make it easier for the model to understand.
- Select features: Use feature selection techniques to remove irrelevant features such as the color of the house or the type of roof.
Further Reading
For more information on feature engineering, check out our Advanced Feature Engineering tutorial.