Feature Vectors
In data science, a feature vector is an n-dimensional vector of numerical features that represent some object, such as an image or a document. Each dimension of the vector corresponds to a specific feature or attribute of the object. Feature vectors are commonly used in machine learning algorithms to train models that can classify or predict new objects based on their features. The process of creating feature vectors involves selecting relevant features, extracting them from the raw data, and transforming them into a numerical format that can be used by the machine learning algorithm. Feature vectors can be created using a variety of techniques, such as bag-of-words, TF-IDF, and word embeddings.
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