Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: ml-statistics.md

layout: global title: Basic Statistics displayTitle: Basic Statistics license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. `\[ \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\wv}{\mathbf{w}} \newcommand{\av}{\mathbf{\alpha}} \newcommand{\bv}{\mathbf{b}} \newcommand{\N}{\mathbb{N}} \newcommand{\id}{\mathbf{I}} \newcommand{\ind}{\mathbf{1}} \newcommand{\0}{\mathbf{0}} \newcommand{\unit}{\mathbf{e}} \newcommand{\one}{\mathbf{1}} \newcommand{\zero}{\mathbf{0}} \]` Table of Contents This will become a table of contents (this text will be scraped). {:toc} 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. <div class="codetabs"> <div data-lang="python" markdown="1"> computes the correlation matrix for the input Dataset of Vectors using the specified method. The output will be a DataFrame that contains the correlation matrix of the column of vectors. {% includeexample python/ml/correlationexample.py %} </div> <div data-lang="scala" markdown="1"> computes the correlation matrix for the input Dataset of Vectors using the specified method. The output will be a DataFrame that contains the correlation matrix of the column of vectors. {% include_example scala/org/apache/spark/examples/ml/CorrelationExample.scala %} </div> <div data-lang="java" markdown="1"> computes the correlation matrix for the input Dataset of Vectors using the specified method. The output will be a DataFrame that contains the correlation matrix of the column of vectors. {% include_example java/org/apache/spark/examples/ml/JavaCorrelationExample.java %} </div> </div> 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. <div class="codetabs"> <div data-lang="python" markdown="1"> Refer to the for details on the API. {% includeexample python/ml/chisquaretestexample.py %} </div> <div data-lang="scala" markdown="1"> Refer to the for details on the API. {% include_example scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the for details on the API. {% include_example java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java %} </div> </div> We provide vector column summary statistics for `Dataframe` through `Summarizer`. Available metrics are the column-wise max, min, mean, sum, variance, std, and number of nonzeros, as well as the total count. <div class="codetabs"> <div data-lang="python" markdown="1"> Refer to the for details on the API. {% includeexample python/ml/summarizerexample.py %} </div> <div data-lang="scala" markdown="1"> The following example demonstrates using to compute the mean and variance for a vector column of the input dataframe, with and without a weight column. {% include_example scala/org/apache/spark/examples/ml/SummarizerExample.scala %} </div> <div data-lang="java" markdown="1"> The following example demonstrates using to compute the mean and variance for a vector column of the input dataframe, with and without a weight column. {% include_example java/org/apache/spark/examples/ml/JavaSummarizerExample.java %} </div> </div>