FeatureVektoren
FeatureVektoren, often translated as feature vectors, are fundamental in machine learning and data analysis. They represent data points in a multi-dimensional space, where each dimension corresponds to a specific characteristic or feature of the data. Essentially, a feature vector is a numerical representation of an object or instance. For example, in image recognition, a feature vector might contain pixel intensity values, color histograms, or shape descriptors. For text analysis, it could include word frequencies or TF-IDF scores.
The process of creating feature vectors is called feature extraction or feature engineering. This involves selecting
Machine learning algorithms operate on these numerical representations. Whether it's a classification task, a regression problem,