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TF-IDF Transformer#

Term Frequency - Inverse Document Frequency is a measurement of how important a word is to a document. The TF-IDF value increases with the number of times a word appears in a document (TF) and is offset by the frequency of the word in the corpus (IDF).


TF-IDF Transformer assumes that its inputs are token frequency vectors such as those created by Word Count Vectorizer.

Interfaces: Transformer, Stateful, Elastic, Reversible, Persistable

Data Type Compatibility: Continuous only


# Name Default Type Description
1 smoothing 1.0 float The amount of additive (Laplace) smoothing to add to the IDFs.
2 dampening false bool Should we apply a sub-linear function to dampen the effect of recurring tokens?


use Rubix\ML\Transformers\TfIdfTransformer;

$transformer = new TfIdfTransformer(2.0, true);

Additional Methods#

Return the document frequencies calculated during fitting:

public dfs() : ?array


  1. S. Robertson. (2003). Understanding Inverse Document Frequency: On theoretical arguments for IDF. 

  2. C. D. Manning et al. (2009). An Introduction to Information Retrieval.