Feature Transformation – ChiSqSelector (Estimator)
Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label
ft_chisq_selector(
x,
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_"),
...
)x |
A |
features_col |
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 |
output_col |
The name of the output column. |
label_col |
Label column name. The column should be a numeric column. Usually this column is output by |
selector_type |
(Spark 2.1.0+) The selector type of the ChisqSelector. Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe". |
fdr |
(Spark 2.2.0+) The upper bound of the expected false discovery rate. Only applicable when selector_type = "fdr". Default value is 0.05. |
fpr |
(Spark 2.1.0+) The highest p-value for features to be kept. Only applicable when selector_type= "fpr". Default value is 0.05. |
fwe |
(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. |
num_top_features |
Number of features that selector will select, ordered by ascending p-value. If the number of features is less than |
percentile |
(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. |
uid |
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 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_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()
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