ml_chisquare_test
From sparklyr v1.5.1
by Yitao Li
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.
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")
# }
Community examples
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