# ft_quantile_discretizer

0th

Percentile

##### Feature Transformation -- QuantileDiscretizer

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.

##### Usage
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.

Other feature transformation routines: ft_binarizer, ft_bucketizer, 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