Feature Transformation -- OneHotEncoder (Transformer)

One-hot encoding maps a column of label indices to a column of binary vectors, with at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. Typically, used with ft_string_indexer() to index a column first.

ft_one_hot_encoder(x, input_col, output_col, drop_last = TRUE,
  uid = random_string("one_hot_encoder_"), ...)



A spark_connection, ml_pipeline, or a tbl_spark.


The name of the input column.


The name of the output column.


Whether to drop the last category. Defaults to TRUE.


A character string used to uniquely identify the feature transformer.


Optional arguments; currently unused.


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 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_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_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