ml_chisquare_test

0th

Percentile

Chi-square hypothesis testing for categorical data.

Conduct Pearson's independence test for every feature against the label. For each feature, the (feature, label) pairs are converted into a contingency matrix for which the Chi-squared statistic is computed. All label and feature values must be categorical.

Usage
ml_chisquare_test(x, features, label)
Arguments
x

A tbl_spark.

features

The name(s) of the feature columns. This can also be the name of a single vector column created using ft_vector_assembler().

label

The name of the label column.

Value

A data frame with one row for each (feature, label) pair with p-values, degrees of freedom, and test statistics.

Aliases
  • ml_chisquare_test
Examples
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

features <- c("Petal_Width", "Petal_Length", "Sepal_Length", "Sepal_Width")

ml_chisquare_test(iris_tbl, features = features, label = "Species")
# }
Documentation reproduced from package sparklyr, version 0.9.3, License: Apache License 2.0 | file LICENSE

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