Feature Transformation - HashingTF (Transformer)
ft_hashing_tf
Description
Maps a sequence of terms to their term frequencies using the hashing trick.
Usage
ft_hashing_tf(
x,
input_col = NULL,
output_col = NULL,
binary = FALSE,
num_features = 2^18,
uid = random_string("hashing_tf_"),
...
)Arguments
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_spark. |
| input_col | The name of the input column. |
| output_col | The name of the output column. |
| binary | 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) |
| num_features | Number of features. Should be greater than 0. (default = 2^18) |
| uid | A character string used to uniquely identify the feature transformer. |
| … | Optional arguments; currently unused. |
Value
The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator or a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the transformer or estimator appended to it. If a tbl_spark, it will return a tbl_spark with the transformation applied to it.
See Also
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(), ft_one_hot_encoder_estimator(), ft_pca(), ft_polynomial_expansion(), 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()