# Feature Transformation -- QuantileDiscretizer

## Usage

ft_quantile_discretizer(x, input_col = NULL, output_col = NULL, n_buckets = 5)

## 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.

## Description

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.

## Details

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_discrete_cosine_transform, ft_elementwise_product, ft_index_to_string, ft_one_hot_encoder, ft_sql_transformer, ft_string_indexer, ft_vector_assembler, sdf_mutate