Feature Transformation - ChiSqSelector (Estimator)
ft_chisq_selector
Description
Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label
Usage
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_"),
...
)Arguments
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_spark. |
| 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 ft_r_formula. |
| 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 ft_r_formula. |
| 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 num_top_features, then this will select all features. Only applicable when selector_type = “numTopFeatures”. The default value of num_top_features is 50. |
| 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. |
Details
In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, returning a tbl_spark.
Value
The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator or a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the transformer or estimator appended to it. If a tbl_spark, it will return a tbl_spark with the transformation applied to it.
See Also
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(), ft_one_hot_encoder_estimator(), 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()