sparklyr (version 1.8.5)

ft_binarizer: Feature Transformation -- Binarizer (Transformer)

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_"),
  ...
)

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.

Arguments

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.

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_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_indexer(), ft_vector_slicer(), ft_word2vec()

Examples

if (FALSE) {
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)
}