ft_standard_scaler: Feature Transformation - StandardScaler (Estimator)

View source: R/ml_feature_standard_scaler.R

ft_standard_scalerR Documentation

Feature Transformation – StandardScaler (Estimator)


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.


  input_col = NULL,
  output_col = NULL,
  with_mean = FALSE,
  with_std = TRUE,
  uid = random_string("standard_scaler_"),



A spark_connection, ml_pipeline, or a tbl_spark.


The name of the input column.


The name of the output column.


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


Whether to scale the data to unit standard deviation. Default: TRUE


A character string used to uniquely identify the feature transformer.


Optional arguments; currently unused.


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.


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

See Also

See https://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in 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_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()


## 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 %>%
    input_col = features,
    output_col = "features_temp"
  ) %>%
    input_col = "features_temp",
    output_col = "features",
    with_mean = TRUE

## End(Not run)

sparklyr documentation built on June 8, 2022, 1:07 a.m.