feature

Type Members

1. final class Binarizer extends Transformer with HasInputCol with HasOutputCol

   :: Experimental :: Binarize a column of continuous features given a threshold.

2. final class Bucketizer extends Model[Bucketizer] with HasInputCol with HasOutputCol

   :: Experimental :: Bucketizer maps a column of continuous features to a column of feature buckets.

3. class ElementwiseProduct extends UnaryTransformer[Vector, Vector, ElementwiseProduct]

   :: Experimental :: Outputs the Hadamard product (i.

4. class HashingTF extends Transformer with HasInputCol with HasOutputCol

   :: Experimental :: Maps a sequence of terms to their term frequencies using the hashing trick.

5. final class IDF extends Estimator[IDFModel] with IDFBase

   :: Experimental :: Compute the Inverse Document Frequency (IDF) given a collection of documents.

6. class IDFModel extends Model[IDFModel] with IDFBase

   :: Experimental :: Model fitted by IDF.

7. class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer]

   :: Experimental :: Normalize a vector to have unit norm using the given p-norm.

8. class OneHotEncoder extends Transformer with HasInputCol with HasOutputCol

   :: Experimental :: A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index.

9. class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion]

   :: Experimental :: Perform feature expansion in a polynomial space.

10. class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer]

    :: Experimental :: A regex based tokenizer that extracts tokens either by using the provided regex pattern to split the text (default) or repeatedly matching the regex (if gaps is true).

11. class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams

    :: Experimental :: Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.

12. class StandardScalerModel extends Model[StandardScalerModel] with StandardScalerParams

    :: Experimental :: Model fitted by StandardScaler.

13. class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase

    :: Experimental :: A label indexer that maps a string column of labels to an ML column of label indices.

14. class StringIndexerModel extends Model[StringIndexerModel] with StringIndexerBase

    :: Experimental :: Model fitted by StringIndexer.

15. class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer]

    :: Experimental :: A tokenizer that converts the input string to lowercase and then splits it by white spaces.

16. class VectorAssembler extends Transformer with HasInputCols with HasOutputCol

    :: Experimental :: A feature transformer that merges multiple columns into a vector column.

17. class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams

    :: Experimental :: Class for indexing categorical feature columns in a dataset of Vector.

18. class VectorIndexerModel extends Model[VectorIndexerModel] with VectorIndexerParams

    :: Experimental :: Transform categorical features to use 0-based indices instead of their original values.

19. final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase

    :: Experimental :: Word2Vec trains a model of Map(String, Vector), i.

20. class Word2VecModel extends Model[Word2VecModel] with Word2VecBase

    :: Experimental :: Model fitted by Word2Vec.