Takes a column with continuous features and outputs a column with binned categorical features. The bin ranges are chosen by taking a sample of the data and dividing it into roughly equal parts. The lower and upper bin bounds will be -Infinity and +Infinity, covering all real values. This attempts to find numBuckets partitions based on a sample of the given input data, but it may find fewer depending on the data sample values.

ft_quantile_discretizer(x, input.col = NULL, output.col = NULL,
  n.buckets = 5L, ...)

Arguments

x
An object (usually a spark_tbl) coercable to a Spark DataFrame.
input.col
The name of the input column(s).
output.col
The name of the output column.
n.buckets
The number of buckets to use.
...
Optional arguments; currently unused.

Details

Note that the result may be different every time you run it, since the sample strategy behind it is non-deterministic.

See also

See http://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in Spark.

Other feature transformation routines: ft_binarizer, ft_bucketizer, ft_count_vectorizer, ft_discrete_cosine_transform, ft_elementwise_product, ft_index_to_string, ft_one_hot_encoder, ft_regex_tokenizer, ft_sql_transformer, ft_string_indexer, ft_tokenizer, ft_vector_assembler, sdf_mutate