Feature Transformation – RobustScaler (Estimator)

R/ml_feature_robust_scaler.R

ft_robust_scaler

Description

RobustScaler removes the median and scales the data according to the quantile range. The quantile range is by default IQR (Interquartile Range, quantile range between the 1st quartile = 25th quantile and the 3rd quartile = 75th quantile) but can be configured. Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and quantile range are then stored to be used on later data using the transform method. Note that missing values are ignored in the computation of medians and ranges.

Usage

ft_robust_scaler( 
  x, 
  input_col = NULL, 
  output_col = NULL, 
  lower = 0.25, 
  upper = 0.75, 
  with_centering = TRUE, 
  with_scaling = TRUE, 
  relative_error = 0.001, 
  uid = random_string("ft_robust_scaler_"), 
  ... 
)

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.
lower	Lower quantile to calculate quantile range.
upper	Upper quantile to calculate quantile range.
with_centering	Whether to center data with median.
with_scaling	Whether to scale the data to quantile range.
relative_error	The target relative error for quantile computation.
uid	A character string used to uniquely identify the feature transformer.
…	Optional arguments; currently unused.

Details

In the case where x is a tbl_spark, the estimator fits against x

to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.

Value

The object returned depends on the class of x.

spark_connection: When x is a spark_connection, the function returns a ml_transformer, a ml_estimator, or one of their subclasses. The object contains a pointer to a Spark Transformer or Estimator object and can be used to compose Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the transformer or estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, a transformer is constructed then immediately applied to the input tbl_spark, 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_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()