# ft_standard_scaler

##### Feature Tranformation -- 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.

##### Usage

```
ft_standard_scaler(x, input_col, output_col, with_mean = FALSE,
with_std = TRUE, dataset = NULL,
uid = random_string("standard_scaler_"), ...)
```

##### 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.

- 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

- dataset
(Optional) A

`tbl_spark`

. If provided, eagerly fit the (estimator) feature "transformer" against`dataset`

. See details.- uid
A character string used to uniquely identify the feature transformer.

- ...
Optional arguments; currently unused.

##### Details

When `dataset`

is provided for an estimator transformer, the function
internally calls `ml_fit()`

against `dataset`

. Hence, the methods for
`spark_connection`

and `ml_pipeline`

will then return a `ml_transformer`

and a `ml_pipeline`

with a `ml_transformer`

appended, respectively. When
`x`

is a `tbl_spark`

, the estimator will be fit against `dataset`

before
transforming `x`

.

When `dataset`

is not specified, the constructor returns a `ml_estimator`

, and,
in the case where `x`

is a `tbl_spark`

, the estimator fits against `x`

then
to obtain a transformer, which is then immediately used to transform `x`

.

##### 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`

##### See Also

See http://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`

, `ft_pca`

,
`ft_polynomial_expansion`

,
`ft_quantile_discretizer`

,
`ft_r_formula`

,
`ft_regex_tokenizer`

,
`ft_sql_transformer`

,
`ft_stop_words_remover`

,
`ft_string_indexer`

,
`ft_tokenizer`

,
`ft_vector_assembler`

,
`ft_vector_indexer`

,
`ft_vector_slicer`

, `ft_word2vec`

*Documentation reproduced from package sparklyr, version 0.8.0, License: Apache License 2.0 | file LICENSE*