697a5e5517
## What changes were proposed in this pull request? Add docs and examples for ```ml.stat.Correlation``` and ```ml.stat.ChiSquareTest```. ## How was this patch tested? Generate docs and run examples manually, successfully. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17994 from yanboliang/spark-20505.
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| layout | title | displayTitle |
|---|---|---|
| global | Basic Statistics | Basic Statistics |
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Table of Contents
- This will become a table of contents (this text will be scraped). {:toc}
Correlation
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
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 %}