f3548837c6
### What changes were proposed in this pull request? Add UnivariateFeatureSelector ### Why are the changes needed? Have one UnivariateFeatureSelector, so we don't need to have three Feature Selectors. ### Does this PR introduce _any_ user-facing change? Yes ``` selector = UnivariateFeatureSelector(featureCols=["x", "y", "z"], labelCol=["target"], featureType="categorical", labelType="continuous", selectorType="numTopFeatures", numTopFeatures=100) ``` Or numTopFeatures ``` selector = UnivariateFeatureSelector(featureCols=["x", "y", "z"], labelCol=["target"], scoreFunction="f_classif", selectorType="numTopFeatures", numTopFeatures=100) ``` ### How was this patch tested? Add Unit test Closes #31160 from huaxingao/UnivariateSelector. Authored-by: Huaxin Gao <huaxing@us.ibm.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
5.2 KiB
| layout | title | displayTitle | license |
|---|---|---|---|
| global | Basic Statistics | Basic Statistics | 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}
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
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 %}
Summarizer
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.
{% 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 %}