ft_min_max_scaler
Feature Tranformation -- MinMaxScaler (Estimator)
Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling
Usage
ft_min_max_scaler(x, input_col, output_col, min = 0, max = 1,
dataset = NULL, uid = random_string("min_max_scaler_"), ...)Arguments
- x
A
spark_connection,ml_pipeline, or atbl_spark.- input_col
The name of the input column.
- output_col
The name of the output column.
- min
Lower bound after transformation, shared by all features Default: 0.0
- max
Upper bound after transformation, shared by all features Default: 1.0
- 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: Whenxis aspark_connection, the function returns aml_transformer, aml_estimator, or one of their subclasses. The object contains a pointer to a SparkTransformerorEstimatorobject and can be used to composePipelineobjects.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the transformer or estimator appended to the pipeline.tbl_spark: Whenxis 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_imputer,
ft_index_to_string,
ft_interaction, ft_lsh,
ft_max_abs_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