Glossary

Dimensionality reduction

What is Dimensionality reduction?

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.

Topics

data analysis
PCA
features

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Related terms

Feature extraction

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.

data preparation
feature engineering
+1

Clustering

Clustering is an unsupervised learning method that divides data points into groups based on their similarity, without predefined categories. It reveals patterns nobody has named before – for example customer segments with similar buying behaviour. With texts, clustering helps sort hundreds of customer enquiries into topics such as delivery, invoicing or complaints and shows where the focus lies.

unsupervised learning
grouping
+1

Template

Overfitting

Overfitting occurs when a model memorises the training data, including random quirks and noise, instead of capturing generally valid patterns. It then appears very accurate on familiar data but performs poorly on new data. A large gap between training and test error is a typical sign. Countermeasures include more or more varied data, simpler models, regularisation, dropout and cross-validation.

model quality
training
+1

Feature engineering and extraction

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.

Machine learning
Data preparation
+1