ft_imputer
Feature Transformation -- Imputer (Estimator)
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+.
Usage
ft_imputer(x, input_cols, output_cols, missing_value = NULL,
strategy = "mean", dataset = NULL, uid = random_string("imputer_"), ...)
Arguments
- x
A
spark_connection
,ml_pipeline
, or atbl_spark
.- input_cols
The names of the input columns
- output_cols
The names of the output columns.
- missing_value
The placeholder for the missing values. All occurrences of
missing_value
will be imputed. Note that null values are always treated as missing.- strategy
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
- dataset
(Optional) A
tbl_spark
. If provided, eagerly fit the (estimator) feature "transformer" againstdataset
. 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
: Whenx
is aspark_connection
, the function returns aml_transformer
, aml_estimator
, or one of their subclasses. The object contains a pointer to a SparkTransformer
orEstimator
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the transformer or estimator appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a transformer is constructed then immediately applied to the inputtbl_spark
, returning atbl_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_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