Imputation estimator for completing missing values, either using the mean or the median of the columns in which the missing values are located. The input columns should be of numeric type. This function requires Spark 2.2.0+.
ft_imputer(x, input_cols, output_cols, missing_value = NULL,
strategy = "mean", dataset = NULL, uid = random_string("imputer_"), ...)
A spark_connection
, ml_pipeline
, or a tbl_spark
.
The names of the input columns
The names of the output columns.
The placeholder for the missing values. All occurrences of
missing_value
will be imputed. Note that null values are always treated
as missing.
The imputation strategy. Currently only "mean" and "median" are supported. If "mean", then replace missing values using the mean value of the feature. If "median", then replace missing values using the approximate median value of the feature. Default: mean
(Optional) A tbl_spark
. If provided, eagerly fit the (estimator)
feature "transformer" against dataset
. See details.
A character string used to uniquely identify the feature transformer.
Optional arguments; currently unused.
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
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
.
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_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_standard_scaler
,
ft_stop_words_remover
,
ft_string_indexer
,
ft_tokenizer
,
ft_vector_assembler
,
ft_vector_indexer
,
ft_vector_slicer
, ft_word2vec