A feature transformer that filters out stop words from input.
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_"),
...
)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.
A spark_connection, ml_pipeline, or a tbl_spark.
The name of the input column.
The name of the output column.
Whether to do a case sensitive comparison over the stop words.
The words to be filtered out.
A character string used to uniquely identify the feature transformer.
Optional arguments; currently unused.
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_estimator(),
ft_one_hot_encoder(),
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()