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 = 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

Aliases
  • ft_quantile_discretizer
Documentation reproduced from package sparklyr, version 0.3.2, License: file LICENSE

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