Feature Transformation - StopWordsRemover (Transformer)
ft_stop_words_remover
Description
A feature transformer that filters out stop words from input.
Usage
ft_stop_words_remover(
x,
input_col = NULL,
output_col = NULL,
case_sensitive = FALSE,
stop_words = ml_default_stop_words(spark_connection(x), "english"),
uid = random_string("stop_words_remover_"),
...
)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. |
| case_sensitive | Whether to do a case sensitive comparison over the stop words. |
| stop_words | The words to be filtered out. |
| 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
ml_default_stop_words
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(), 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_string_indexer(), ft_tokenizer(), ft_vector_assembler(), ft_vector_indexer(), ft_vector_slicer(), ft_word2vec()