Feature extraction is the process of deriving meaningful values from raw data so that a model can process them efficiently. Instead of millions of individual pixels, a system might use edges, colours or shapes; texts are turned into word weights or embedding vectors. Good features reduce data volume, lower computing effort and often improve accuracy. Feature extraction is a core part of feature engineering.
Feature engineering means selecting, transforming and creating attributes (features) from raw data so that a learning algorithm can detect patterns more easily. Feature extraction derives compact, meaningful values from complex data such as text, images or signals. Example: individual purchase records are turned into features like ‘days since last purchase’ or ‘average order value’, which can noticeably improve a churn prediction.
Dimensionality reduction lowers the number of features in a data set without losing the essential information. Well-known methods include principal component analysis (PCA) as well as t-SNE and UMAP, which are often used for visualisation. It speeds up computation, reduces the risk of overfitting and makes data easier to grasp – for example when hundreds of customer attributes are mapped onto a two-dimensional chart on which similar customers sit close together.
An embedding vector (embedding) is a list of numbers that represents the meaning of a word, sentence or document so that texts with similar content lie close together. This makes it possible to recognise, for example, that 'cancellation' and 'ending the contract' are related even though they share no words. Embeddings underpin many semantic search systems, recommendation services and RAG applications; they are computed by specially trained models.
Pattern recognition is the automatic identification of regularities, structures or recurring features in data such as images, sounds, texts or measurements. Systems assign new observations to known categories or discover groups without predefined labels. Applications include facial recognition, handwriting recognition, quality inspection in manufacturing and spotting unusual transactions. The discipline is an important foundation of machine learning.
