Feature Transformation -- VectorIndexer (Estimator)

Indexing categorical feature columns in a dataset of Vector.

ft_vector_indexer(x, input_col = NULL, output_col = NULL,
  max_categories = 20, uid = random_string("vector_indexer_"), ...)

Arguments

x

A spark_connection, ml_pipeline, or a tbl_spark.

input_col

The name of the input column.

output_col

The name of the output column.

max_categories

Threshold for the number of values a categorical feature can take. If a feature is found to have > max_categories values, then it is declared continuous. Must be greater than or equal to 2. Defaults to 20.

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

Value

The object returned depends on the class of x.

  • spark_connection: When x is a spark_connection, the function returns a ml_transformer, a ml_estimator, or one of their subclasses. The object contains a pointer to a Spark Transformer or Estimator object and can be used to compose Pipeline objects.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the transformer or estimator appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, a transformer is constructed then immediately applied to the input tbl_spark, returning a tbl_spark

Details

In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.

See also