SparkQA commented on issue #27549: [SPARK-30803][DOCS] Fix the home page link for Scala API document URL: https://github.com/apache/spark/pull/27549#issuecomment-586600467 **[Test build #118479 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/118479/testReport)** for PR 27549 at commit [`2a5a571`](https://github.com/apache/spark/commit/2a5a57178abcf69942af3a89c8f09a5600ebb6fe). * This patch passes all tests. * This patch merges cleanly. * This patch adds the following public classes _(experimental)_: * `[Tokenization](http://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) is the process of taking text (such as a sentence) and breaking it into individual terms (usually words). A simple [Tokenizer](api/scala/org/apache/spark/ml/feature/Tokenizer.html) class provides this functionality. The example below shows how to split sentences into sequences of words.` * `[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. A [PCA](api/scala/org/apache/spark/ml/feature/PCA.html) class trains a model to project vectors to a low-dimensional space using PCA. The example below shows how to project 5-dimensional feature vectors into 3-dimensional principal components.` * `[Polynomial expansion](http://en.wikipedia.org/wiki/Polynomial_expansion) is the process of expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions. A [PolynomialExpansion](api/scala/org/apache/spark/ml/feature/PolynomialExpansion.html) class provides this functionality. The example below shows how to expand your features into a 3-degree polynomial space.` * ` * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.html) have been changed because of a modification to the case class fields. This could be an issue for users who use `BoostingStrategy` to set GBT parameters.` * `* *(Breaking change)* The return value of [`LDA.run`](api/scala/org/apache/spark/mllib/clustering/LDA.html) has changed. It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`. The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.` * `A [Converter](api/scala/org/apache/spark/api/python/Converter.html) trait is provided` * `In Scala, you have to extend the class `ForeachWriter` ([docs](api/scala/org/apache/spark/sql/ForeachWriter.html)).`
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