Class  Description 

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

Bucketizer 
:: Experimental ::
Bucketizer maps a column of continuous features to a column of feature buckets. 
ElementwiseProduct 
:: Experimental ::
Outputs the Hadamard product (i.e., the elementwise product) of each input vector with a
provided "weight" vector.

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

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

IDFModel  
Normalizer 
:: Experimental ::
Normalize a vector to have unit norm using the given pnorm.

OneHotEncoder 
:: Experimental ::
A onehot encoder that maps a column of category indices to a column of binary vectors, with
at most a single onevalue per row that indicates the input category index.

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

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

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

StringIndexerModel 
:: Experimental ::
Model fitted by
StringIndexer . 
Tokenizer 
:: Experimental ::
A tokenizer that converts the input string to lowercase and then splits it by white spaces.

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

VectorIndexer 
:: Experimental ::
Class for indexing categorical feature columns in a dataset of
Vector . 
VectorIndexer.CategoryStats 
Helper class for tracking unique values for each feature.

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

Word2Vec 
:: Experimental ::
Word2Vec trains a model of
Map(String, Vector) , i.e. 
Word2VecModel 
:: Experimental ::
Model fitted by
Word2Vec . 