Feature engineering in the context of data preprocessing is the process of using domain knowledge to create features (i.e., variables) that make machine learning algorithms work more effectively. These features may be derived from raw data, but can encapsulate important information in a format that’s more suitable for these algorithms to process.

For example, imagine you’re working with a dataset of timestamps and you want to predict a certain event based on the time of the year. The raw timestamp data may not be very useful to your model, but if you engineer a feature that captures the particular season (spring, summer, fall, or winter), it may significantly improve your model’s performance.

Feature engineering can involve a wide variety of activities, including:

  • Binning: Converting a numerical variable into different ‘bins’ or categories. For example, you could bin ages into groups like ‘0-18′, ’19-35′, ’36-60′, and ’60+’.
  • Polynomial features: Creating new features based on polynomial combinations of existing ones. This is often useful in regression models to capture relationships between features that aren’t linear.
  • Interaction features: Creating new features that represent the interaction between two existing features. For instance, if you have features ‘height’ and ‘width’, you might add a feature ‘area’ which is the product of the two.
  • Aggregation: Creating summary statistics like count, mean, sum, or variance across multiple data points.
  • Encoding categorical variables: Transforming categorical variables into a format that can be used by machine learning algorithms, such as one-hot encoding or ordinal encoding.
  • Handling missing values: Deciding how to handle missing data, which could involve removing these instances or imputing a value based on other data.
  • Scaling features: Some machine learning models work best when all features are on a similar scale. Standard scaling (subtracting the mean and dividing by the standard deviation) and min-max scaling (scaling to a range between 0 and 1) are common approaches.

Feature engineering is an essential part of the machine learning pipeline, but it can be time-consuming and requires domain knowledge. Automated feature engineering, provided by libraries like featuretools in Python, can help automate this process, but manually created features can often provide valuable improvements to a model’s performance.