View source: R/ml_feature_binarizer.R
ft_binarizer | R Documentation |
Apply thresholding to a column, such that values less than or equal to the
threshold
are assigned the value 0.0, and values greater than the
threshold are assigned the value 1.0. Column output is numeric for
compatibility with other modeling functions.
ft_binarizer(
x,
input_col,
output_col,
threshold = 0,
uid = random_string("binarizer_"),
...
)
x |
A |
input_col |
The name of the input column. |
output_col |
The name of the output column. |
threshold |
Threshold used to binarize continuous features. |
uid |
A character string used to uniquely identify the feature transformer. |
... |
Optional arguments; currently unused. |
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.
Other feature transformers:
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_stop_words_remover()
,
ft_string_indexer()
,
ft_tokenizer()
,
ft_vector_assembler()
,
ft_vector_indexer()
,
ft_vector_slicer()
,
ft_word2vec()
## Not run:
library(dplyr)
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
iris_tbl %>%
ft_binarizer(
input_col = "Sepal_Length",
output_col = "Sepal_Length_bin",
threshold = 5
) %>%
select(Sepal_Length, Sepal_Length_bin, Species)
## End(Not run)
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