# ft_quantile_discretizer

##### 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 = 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.

##### 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_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`

*Documentation reproduced from package sparklyr, version 0.3.6, License: file LICENSE*