An Estimator that chooses all sparse features observed when training, and produces a transformer which builds a sparse vector out of them.
Caches an RDD at a given point within a Pipeline.
Given a set of class labels, returns a binary vector that indicates when each class is present.
Given a class label, returns a binary vector that indicates when that class is present.
An Estimator that chooses the most frequently observed sparse features when training, and produces a transformer which builds a sparse vector out of them
Transformer to densify vectors into DenseVectors.
This class performs a no-op on its input.
Randomly shuffle the rows of an RDD within a pipeline.
A transformer which given a feature space, maps features of the form (feature id, value) into a sparse vector
Transformer to convert vectors into SparseVectors.
Transformer that returns the indices of the largest k values of the vector, in order
Concats a Seq of DenseVectors into a single DenseVector.
This transformer splits the input vector into a number of blocks.
Converts float matrix to a double matrix.
Flattens a matrix into a vector.
Transformer that returns the index of the largest value in the vector
Object to allow creating top k classifier w/o new