Indexing categorical feature columns in a dataset of Vector.
ft_vector_indexer(
x,
input_col = NULL,
output_col = NULL,
handle_invalid = "error",
max_categories = 20,
uid = random_string("vector_indexer_"),
...
)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.
(Spark 2.1.0+) Param for how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Default: "error"
Threshold for the number of values a categorical feature can take. If a feature is found to have > max_categories values, then it is declared continuous. Must be greater than or equal to 2. Defaults to 20.
A character string used to uniquely identify the feature transformer.
Optional arguments; currently unused.
In the case where x is a tbl_spark, the estimator
fits against x to obtain a transformer, returning a tbl_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_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_slicer(),
ft_word2vec()