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Showing content from http://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/CountVectorizerParams.html below:

CountVectorizerParams (Spark 4.0.0 JavaDoc)

All Superinterfaces:

HasInputCol, HasOutputCol, Identifiable, Params, Serializable

All Known Implementing Classes:

CountVectorizer, CountVectorizerModel

• Method Summary

  Binary toggle to control the output vector values.

  boolean

  double

  double

  double

  int

  Specifies the maximum number of different documents a term could appear in to be included in the vocabulary.

  Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.

  Filter to ignore rare words in a document.

  Validates and transforms the input schema.

  Max size of the vocabulary.

  Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

• Method Details
  • binary

    Binary toggle to control the output vector values. If True, all nonzero counts (after minTF filter applied) are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default: false

    Returns:

    (undocumented)

  • getBinary

    boolean getBinary()

  • getMaxDF

    double getMaxDF()

  • getMinDF

    double getMinDF()

  • getMinTF

    double getMinTF()

  • getVocabSize

    int getVocabSize()

  • maxDF

    Default: (2^63^) - 1

    Returns:

    (undocumented)

  • minDF

    Default: 1.0

    Returns:

    (undocumented)

  • minTF

    Filter to ignore rare words in a document. For each document, terms with frequency/count less than the given threshold are ignored. If this is an integer greater than or equal to 1, then this specifies a count (of times the term must appear in the document); if this is a double in [0,1), then this specifies a fraction (out of the document's token count).

    Note that the parameter is only used in transform of CountVectorizerModel and does not affect fitting.

    Default: 1.0

    Returns:

    (undocumented)

  • validateAndTransformSchema

    Validates and transforms the input schema.

  • vocabSize

    Default: 2^18^

    Returns:

    (undocumented)

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