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16.  def test(dataset: DataFrame, featuresCol: String, labelCol: String, flatten: Boolean): DataFrame
    dataset

    DataFrame of categorical labels and categorical features. Real-valued features will be treated as categorical for each distinct value.

    featuresCol

    Name of features column in dataset, of type Vector (VectorUDT)

    labelCol

    Name of label column in dataset, of any numerical type

    flatten

    If false, the returned DataFrame contains only a single Row, otherwise, one row per feature.

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    @Since( "3.1.0" )

17.  def test(dataset: DataFrame, featuresCol: String, labelCol: String): DataFrame

    Conduct Pearson's independence test for every feature against the label.

    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.

    The null hypothesis is that the occurrence of the outcomes is statistically independent.

    dataset

    DataFrame of categorical labels and categorical features. Real-valued features will be treated as categorical for each distinct value.

    featuresCol

    Name of features column in dataset, of type Vector (VectorUDT)

    labelCol

    Name of label column in dataset, of any numerical type

    returns

    DataFrame containing the test result for every feature against the label. This DataFrame will contain a single Row with the following fields:

    • pValues: Vector
    • degreesOfFreedom: Array[Int]
    • statistics: Vector Each of these fields has one value per feature.

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    @Since( "2.2.0" )

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