Perform feature expansion in a polynomial space. E.g. take a 2-variable feature vector as an example: (x, y), if we want to expand it with degree 2, then we get (x, x * x, y, x * y, y * y).
ft_polynomial_expansion(
x,
input_col = NULL,
output_col = NULL,
degree = 2,
uid = random_string("polynomial_expansion_"),
...
)The object returned depends on the class of x.
spark_connection: When x is a spark_connection, the function returns a ml_transformer,
a ml_estimator, or one of their subclasses. The object contains a pointer to
a Spark Transformer or Estimator object and can be used to compose
Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with
the transformer or estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, a transformer is constructed then
immediately applied to the input tbl_spark, returning a tbl_spark
A spark_connection, ml_pipeline, or a tbl_spark.
The name of the input column.
The name of the output column.
The polynomial degree to expand, which should be greater than equal to 1. A value of 1 means no expansion. Default: 2
A character string used to uniquely identify the feature transformer.
Optional arguments; currently unused.
See https://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in Spark.
Other feature transformers:
ft_binarizer(),
ft_bucketizer(),
ft_chisq_selector(),
ft_count_vectorizer(),
ft_dct(),
ft_elementwise_product(),
ft_feature_hasher(),
ft_hashing_tf(),
ft_idf(),
ft_imputer(),
ft_index_to_string(),
ft_interaction(),
ft_lsh,
ft_max_abs_scaler(),
ft_min_max_scaler(),
ft_ngram(),
ft_normalizer(),
ft_one_hot_encoder_estimator(),
ft_one_hot_encoder(),
ft_pca(),
ft_quantile_discretizer(),
ft_r_formula(),
ft_regex_tokenizer(),
ft_robust_scaler(),
ft_sql_transformer(),
ft_standard_scaler(),
ft_stop_words_remover(),
ft_string_indexer(),
ft_tokenizer(),
ft_vector_assembler(),
ft_vector_indexer(),
ft_vector_slicer(),
ft_word2vec()