Machine learning models often work with datasets containing many different variables, these are known as features or attributes. Although they provide useful information, not all of them are equally relevant to a specific task. Having too many features increases computational requirements thus making patterns harder to identify. Feature selection and feature extraction are two techniques used to address this challenge by simplifying data, each in different ways.
Feature selection involves identifying the most relevant features in an existing dataset and retaining those that are useful for the task. Rather than transforming the data, it removes unnecessary or less informative variables while preserving the meaning of the features that remain. For instance, a retailer developing a model to predict customer spending might have information about customer locations, purchase histories, and account details. Feature selection helps identify which variables provide the most useful information for predicting spending, allowing less relevant variables to be excluded.
An advantage of feature selection is that it makes models easier to interpret. Since the selected features retain their original meaning, it’s often easier to understand which variables contribute to a prediction. Reducing the number of features also simplifies data processing and limits the influence of irrelevant information. However, the effectiveness of this approach depends on selecting features that retain the information needed for the intended task.
Feature extraction takes a different approach by transforming the original features into a new, smaller set of features. Instead of simply removing variables, it combines or reorganises information from the original dataset to produce a more compact representation. These newly generated features may capture important patterns that are difficult to find when examining each individual variable.
Principal Component Analysis (PCA) is a well known type of feature extraction. It transforms numerical variables into principal components that capture as much of the data’s variation as possible, with the first components representing the greatest amounts of variation. Other techniques, including Non Negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD), can also be used to create more compact data representations. These approaches are particularly helpful when working with large datasets or identifying underlying patterns. However, because extracted features are transformed combinations of the original variables, they can be more difficult to interpret.
The main difference between the two techniques is therefore what happens to the original information. Feature selection keeps a subset of the existing features, whereas feature extraction creates new features from the information available. Feature selection can be preferable when interpretability is important or when certain variables need to retain their original value. Feature extraction may be more suitable when datasets contain many related variables and a compact representation could improve subsequent analysis. The two approaches are also used together, depending on the dataset and the requirements of the model.
Oracle Machine Learning supports both feature selection and feature extraction as part of its machine learning capabilities. Its feature selection functionality can help identify relevant attributes for predictive modelling, while feature extraction algorithms such as PCA, SVD, NMF and Explicit Semantic Analysis can transform data into a smaller set of features. These capabilities allow users to simplify datasets and prepare information for further analysis within Oracle’s database environment.
Overall, feature selection and feature extraction both make machine learning more efficient by reducing the complexity of data. However, choosing between them depends on the priorities, whether it’s to keep the most useful original information or to create a new representation capturing important correlations. Understanding this distinction helps companies prepare their data effectively with the right approach suited to their analytical goals.
