Feature Transformation - Imputer (Estimator)
ft_imputer
Description
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 = NULL,
output_cols = NULL,
missing_value = NULL,
strategy = "mean",
uid = random_string("imputer_"),
...
)Arguments
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_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 |
| 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_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_standard_scaler(), ft_stop_words_remover(), ft_string_indexer(), ft_tokenizer(), ft_vector_assembler(), ft_vector_indexer(), ft_vector_slicer(), ft_word2vec()