anchor.diff:  Throughout the book, we'll make those schemas so you can get the big picture of
   each machine learning application we'll build.
   
  -About the book
  ---------------
  +== About the book
   
   This book won't be an exhaustive tour of the API proposed in Spark ML. Rather,
   it is a guide that will get you from an idea for a machine learning application
  
positive.code: The bottom row represents the _DataFrames_ at every step and the produced
  _Transformer_ which is a `DecisionTreeClassificationModel`.
  
  .PipelineModel transformation
  image::../images/pipeline_transform.svg[]
  
  The top row represents the _PipelineModel_ produced when calling `fit()` on the
  
negative.code: any trouble translating them in the language of your choice at no performance
  cost.
  
  == Core Spark concepts
  
  In this section, we'll dig a bit deeper into the core concepts we'll use
  throughout the book: _DataFrame_ and _Dataset_.
  
  Both _DataFrames_ and _Datasets_ can be built from a variety of sources such as
  JSON, Parquet, tables in a database accessible through JDBC, Hive tables or
  _RDDs_.
  
  === DataFrame
  
  .An example DataFrame with 3 columns and 3 rows
  [options="header"]
  |==================
  | Column 1 | Column 2 | Column 3
  