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DOC: extending the user guide material about data ops #1532

Description

@rcap107

Below there is the outline of the section of the user guide relative to the DataOps. I have highlighted in bold the concepts I think should be in the user guide before the release, while the others (mostly more advanced stuff) will be added in alter PRs.

The reason for this issue is to gather feedback on what people think is important and should be in the user guide, and what instead can be de-prioritized for the time being.

Relevant PRs:

DataOps

  • .skb.if_else and .skb.match, difference with choose_from(...).if_else and choose_from(...).match
  • as_data_op: turn any object into in expression – make its methods lazy; give it the capabilities of expressions; a way to give a name to an arbitrary value and replace it at fit or predict time

Reporting

  • Partial graph (mention vars)
  • Parallel coordinate plot

From DataOps to prediction: building and using learners

  • Applying expressions to column subsets
    • select + transformations + concat
  • Concatenating transformers
  • Finding a fitted estimator to inspect its attributes
  • skb.applied_estimator

Arbitrary code in pipelines: deferred, apply_func, and as_expr

  • Arguments and default arguments
  • Benefits of putting deferred functions in a module (get pickled by name rather than by value in cloudpickle)

Evaluating a learner

  • Custom splitter functions
  • The unsupervised parameter of apply: when y is needed for score but not for fit
  • freeze_after_fit

Tuning choices in a DataOps plan

  • choosing between completely separate pipelines using choose_from as last step

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