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.

ft_binarizer(x, input_col, output_col, threshold = 0,
  uid = random_string("binarizer_"), ...)

Arguments

x

A spark_connection, ml_pipeline, or a tbl_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: 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

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_count_vectorizer, ft_dct, ft_elementwise_product, ft_hashing_tf, ft_idf, ft_index_to_string, ft_ngram, ft_one_hot_encoder, ft_pca, ft_quantile_discretizer, ft_r_formula, ft_regex_tokenizer, ft_sql_transformer, ft_stop_words_remover, ft_string_indexer, ft_tokenizer, ft_vector_assembler, ft_word2vec