One-hot encoding maps a column of label indices to a column of binary vectors, with at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features.
ft_one_hot_encoder(x, input_col = NULL, output_col = NULL)An object (usually a spark_tbl) coercable to a Spark DataFrame.
The name of the input column(s).
The name of the output column.
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_quantile_discretizer,
ft_sql_transformer,
ft_string_indexer,
ft_vector_assembler,
sdf_mutate