Maps a sequence of terms to their term frequencies using the hashing trick.
ft_hashing_tf(x, input_col = NULL, output_col = NULL, binary = FALSE,
num_features = 2^18, uid = random_string("hashing_tf_"), ...)A spark_connection, ml_pipeline, or a tbl_spark.
The name of the input column.
The name of the output column.
Binary toggle to control term frequency counts.
If true, all non-zero counts are set to 1. This is useful for discrete
probabilistic models that model binary events rather than integer
counts. (default = FALSE)
Number of features. Should be greater than 0. (default = 2^18)
A character string used to uniquely identify the feature transformer.
Optional arguments; currently unused.
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
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 transformers: ft_binarizer,
ft_bucketizer,
ft_chisq_selector,
ft_count_vectorizer, ft_dct,
ft_elementwise_product,
ft_feature_hasher, 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_polynomial_expansion,
ft_quantile_discretizer,
ft_r_formula,
ft_regex_tokenizer,
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