ft_standard_scaler {sparklyr} | R Documentation |
Feature Transformation – StandardScaler (Estimator)
Description
Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
Usage
ft_standard_scaler(
x,
input_col = NULL,
output_col = NULL,
with_mean = FALSE,
with_std = TRUE,
uid = random_string("standard_scaler_"),
...
)
Arguments
x |
A |
input_col |
The name of the input column. |
output_col |
The name of the output column. |
with_mean |
Whether to center the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. Default: FALSE |
with_std |
Whether to scale the data to unit standard deviation. Default: TRUE |
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, returning a tbl_spark
.
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_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()
,
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_stop_words_remover()
,
ft_string_indexer()
,
ft_tokenizer()
,
ft_vector_assembler()
,
ft_vector_indexer()
,
ft_vector_slicer()
,
ft_word2vec()
Examples
## Not run:
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
features <- c("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")
iris_tbl %>%
ft_vector_assembler(
input_col = features,
output_col = "features_temp"
) %>%
ft_standard_scaler(
input_col = "features_temp",
output_col = "features",
with_mean = TRUE
)
## End(Not run)