.. _fwflag_test_c2r:
============
--test_c2r
============
Switch
======

--test_c2r

Description
===========

Trains and tests a "classify-to-regression" model: first classifies groups into bins, then regresses within/across those classifications to predict the original continuous outcome.

Argument and Default Value
==========================

No argument. Boolean switch, default: ``False``.

Details
=======

**Verified requirement, not fully exercised:** ``ClassifyToRegressionPredictor`` looks up outcomes using a ``bin_<outcome>`` column name (e.g. ``bin_age`` for an outcome named ``age``) rather than the plain outcome column — confirmed by running against a table without one and seeing ``Loading Outcomes and Getting Groups for: {'bin_age'}`` followed by a "no such column: bin_age" error. You need a pre-binarized version of your outcome in the outcome table under that naming convention (e.g. via :doc:`fwflag_categorical`) before this switch can run. Not independently verified past this point due to time constraints in this pass.

Other Switches
==============

Required Switches:

* :doc:`fwflag_d`, :doc:`fwflag_c`, :doc:`fwflag_t`
* :doc:`fwflag_f`
* :doc:`fwflag_outcome_table`, :doc:`fwflag_outcomes` (with a matching ``bin_<outcome>`` column present)

Example Commands
================

Using the packaged ``dla_tutorial`` dataset (see :doc:`../../tutorials/tut_dla`), assuming a ``bin_age`` column exists in ``blog_outcomes``:

.. code-block:: bash

	dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1to3gram$msgs$user_id$0_05' \
	--outcome_table blog_outcomes --outcomes age --test_c2r