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Table of Contents
Calculating the correlation between two series of data is a common operation in Statistics. In spark.ml we provide the flexibility to calculate pairwise correlations among many series. The supported correlation methods are currently Pearson‘s and Spearman’s correlation.
{% include_example scala/org/apache/spark/examples/ml/CorrelationExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaCorrelationExample.java %}
{% include_example python/ml/correlation_example.py %}
Hypothesis testing is a powerful tool in statistics to determine whether a result is statistically significant, whether this result occurred by chance or not. spark.ml currently supports Pearson's Chi-squared ( $\chi^2$) tests for independence.
ChiSquareTest conducts 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.
{% include_example scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java %}
{% include_example python/ml/chi_square_test_example.py %}
We provide vector column summary statistics for Dataframe through Summarizer. Available metrics are the column-wise max, min, mean, variance, and number of nonzeros, as well as the total count.
{% include_example scala/org/apache/spark/examples/ml/SummarizerExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaSummarizerExample.java %}
{% include_example python/ml/summarizer_example.py %}