![]() ![]() Term frequency $TF(t, d)$ is the number of times that term $t$ appears in document $d$, whileĭocument frequency $DF(t, D)$ is the number of documents that contains term $t$. Denote a term by $t$, a document by $d$, and the corpus by $D$. Is a feature vectorization method widely used in text mining to reflect the importance of a term Term frequency-inverse document frequency (TF-IDF) Bucketed Random Projection for Euclidean Distance.Locality Sensitive Hashing (LSH): This class of algorithms combines aspects of feature transformation with other algorithms. ![]() Selection: Selecting a subset from a larger set of features.Transformation: Scaling, converting, or modifying features.Extraction: Extracting features from “raw” data.This section covers algorithms for working with features, roughly divided into these groups: Extracting, transforming and selecting features
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