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, ...)An object (usually a spark_tbl) coercable to a Spark DataFrame.
The name of the input column(s).
The name of the output column.
The number of buckets to use.
Optional arguments; currently unused.
Note that the result may be different every time you run it, since the sample strategy behind it is non-deterministic.
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