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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 proportionally (linearly) 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).

Note

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

Interfaces: Transformer, Stateful, Elastic, Persistable

Data Type Compatibility: Continuous only

Parameters

#	Name	Default	Type	Description
1	smoothing	1.0	float	The amount of additive (Laplace) smoothing to add to the inverse document frequencies (IDFs).

Example

use Rubix\ML\Transformers\TfIdfTransformer;

$transformer = new TfIdfTransformer(1.0);

Additional Methods

Return the document frequencies calculated during fitting:

public dfs() : ?array

References

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

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Last update: 2021-03-03