Package org.apache.spark.ml.feature

Class Summary

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 element-wise 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 p-norm.
OneHotEncoder	:: 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.
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 0-based indices instead of their original values.
Word2Vec	:: Experimental :: Word2Vec trains a model of Map(String, Vector), i.e.
Word2VecModel	:: Experimental :: Model fitted by Word2Vec.