Feature Transformation - Binarizer (Transformer)
ft_binarizer
Description
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.
Usage
ft_binarizer(
x,
input_col,
output_col,threshold = 0,
uid = random_string("binarizer_"),
... )
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. |
threshold | Threshold used to binarize continuous features. |
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_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()
Examples
library(dplyr)
<- spark_connect(master = "local")
sc <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
iris_tbl
%>%
iris_tbl ft_binarizer(
input_col = "Sepal_Length",
output_col = "Sepal_Length_bin",
threshold = 5
%>%
) select(Sepal_Length, Sepal_Length_bin, Species)