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  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

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  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

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    spark
  • package stat
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    ml
  • package distribution
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    stat
  • ChiSquareTest
  • Correlation
  • KolmogorovSmirnovTest
  • Summarizer
  • SummaryBuilder
o

org.apache.spark.ml.stat

ChiSquareTest

object ChiSquareTest

Chi-square hypothesis testing for categorical data.

See Wikipedia for more information on the Chi-squared test.

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@Since("2.2.0")
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ChiSquareTest.scala
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  15. 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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  16. 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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