.. _fwflag_predict_classifiers_to_outcome_table:
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--predict_classifiers_to_outcome_table
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Switch
======

--predict_classifiers_to_outcome_table NAME

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

Predicts classes with a previously-trained classifier and writes them into a new outcome table, rather than a feature table (see :doc:`fwflag_predict_classifiers_to_feats` for the feature-table version).

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

A name used to build the destination table (``p_<model>$<name>``). Default: ``None``.

Details
=======

Verified end-to-end. **Requires a pre-trained, pre-loaded model** — :doc:`fwflag_load_models`/:doc:`fwflag_picklefile` (trained earlier via :doc:`fwflag_train_classifiers` + :doc:`fwflag_save_models`). Without one, it fails with ``IndexError: list index out of range`` while trying to align features against an empty model. With a real loaded SVC model, produced a table (``p_svc$<name>``, one row per group with the predicted class, e.g. ``743739|0``).

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

Required Switches:

* :doc:`fwflag_d`, :doc:`fwflag_c`, :doc:`fwflag_t`
* :doc:`fwflag_f`
* :doc:`fwflag_load_models`, :doc:`fwflag_picklefile`

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

Using the packaged ``dla_tutorial`` dataset (see :doc:`../../tutorials/tut_pickle_build` and :doc:`../../tutorials/tut_pickle_apply`):

.. 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 is_student --train_classifiers --save_models \
	--picklefile is_student.pickle

	dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1to3gram$msgs$user_id$0_05' \
	--load_models --picklefile is_student.pickle --predict_classifiers_to_outcome_table pred_is_student