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Indexing categorical feature columns in a dataset of Vector.

Usage

ft_vector_indexer(
  x,
  input_col = NULL,
  output_col = NULL,
  handle_invalid = "error",
  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.

handle_invalid

(Spark 2.1.0+) Param for how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Default: "error"

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. If it is a spark_connection, the function returns a ml_estimator or a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the transformer or estimator appended to it. If a tbl_spark, it will return a tbl_spark with the transformation applied to it.

Details

In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, returning a tbl_spark.