Despite being vital to machine learning, feature engineering is not always given due attention. Feature engineering is a supporting step in machine learning modeling, but with a smart approach to data selection, it can increase a model’s efficiency and lead to more accurate results. It involves extracting meaningful features from raw data, sorting features, dismissing duplicate records, and modifying some data columns to obtain new features more relevant to your goals.
From this article, you will learn what feature engineering is and how it can be used to improve your machine learning models. We’ll also discuss different types and techniques of feature engineering and what each type is used for.
Why is feature engineering essential?
Feature engineering is necessary for designing accurate predictive models to solve problems while decreasing the time and computation resources needed.
The features of your data have a direct impact on the predictive models you train and the results you can get with them. Even if your data for analysis is not ideal, you can still get the outcomes you are looking for with a good set of features.
But what is a feature? Features are measurable data elements used for analysis. In datasets, they appear as columns. So, by choosing the most relevant pieces of data, we achieve more accurate predictions for the model.
Another important reason for using feature engineering is that it enables you to cut time spent on data analysis.
What is feature engineering?
Feature engineering is a machine learning technique that transforms available datasets into sets of figures essential for a specific task. This process involves:
- Performing data analysis and correcting inconsistencies (like incomplete, incorrect data or anomalies).
- Deleting variables that do not influence model behavior.
- Dismissing duplicates and correlating records and, sometimes, carrying out data normalization.
