ft_binarizer
Feature Transformation -- Binarizer (Transformer)
Apply thresholding to a column, such that values less than or equal to the
threshold are assigned the value 0.0, and values greater than the
threshold are assigned the value 1.0. Column output is numeric for
compatibility with other modeling functions.
Usage
ft_binarizer(x, input_col, output_col, threshold = 0,
uid = random_string("binarizer_"), ...)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.
- threshold
Threshold used to binarize continuous features.
- uid
A character string used to uniquely identify the feature transformer.
- ...
Optional arguments; currently unused.
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_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_standard_scaler,
ft_stop_words_remover,
ft_string_indexer,
ft_tokenizer,
ft_vector_assembler,
ft_vector_indexer,
ft_vector_slicer, ft_word2vec