ft_chisq_selector: Feature Transformation - ChiSqSelector (Estimator)

View source: R/ml_feature_chisq_selector.R

ft_chisq_selectorR Documentation

Feature Transformation – ChiSqSelector (Estimator)


Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label


  features_col = "features",
  output_col = NULL,
  label_col = "label",
  selector_type = "numTopFeatures",
  fdr = 0.05,
  fpr = 0.05,
  fwe = 0.05,
  num_top_features = 50,
  percentile = 0.1,
  uid = random_string("chisq_selector_"),



A spark_connection, ml_pipeline, or a tbl_spark.


Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.


The name of the output column.


Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.


(Spark 2.1.0+) The selector type of the ChisqSelector. Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe".


(Spark 2.2.0+) The upper bound of the expected false discovery rate. Only applicable when selector_type = "fdr". Default value is 0.05.


(Spark 2.1.0+) The highest p-value for features to be kept. Only applicable when selector_type= "fpr". Default value is 0.05.


(Spark 2.2.0+) The upper bound of the expected family-wise error rate. Only applicable when selector_type = "fwe". Default value is 0.05.


Number of features that selector will select, ordered by ascending p-value. If the number of features is less than num_top_features, then this will select all features. Only applicable when selector_type = "numTopFeatures". The default value of num_top_features is 50.


(Spark 2.1.0+) Percentile of features that selector will select, ordered by statistics value descending. Only applicable when selector_type = "percentile". Default value is 0.1.


A character string used to uniquely identify the feature transformer.


Optional arguments; currently unused.


In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.


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 https://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_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_estimator(), ft_one_hot_encoder(), 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()

sparklyr documentation built on Sept. 2, 2023, 9:06 a.m.