.. _fwflag_predict_cv_to_feats:
======================
--predict_cv_to_feats
======================
Switch
======

--predict_cv_to_feats NAME

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

Runs N-fold cross-validated regression prediction and writes the out-of-fold predictions into a new feature table, without needing a pre-saved model. Aliased as ``--predict_combo_to_feats``, ``--predict_regression_all_to_feats``.

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

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

Details
=======

Verified table naming and most of the pipeline work correctly — table creation, N-fold splitting, and prediction all ran (``[Inserting Predictions as Feature values for age]``).

**Known issue — broken in SQLite mode.** The final insert calls ``mm.executeWriteMany(...)`` with SQL built using MySQL's ``%s`` parameter placeholders. SQLite's ``sqlite3`` driver expects ``?`` placeholders instead, so ``writeCursor.executemany(sql, rows)`` fails with ``sqlite3.OperationalError: near "%": syntax error`` — different root cause from most other SQLite issues found in this pass (a placeholder-style mismatch, not a MySQL-only SQL statement). Works against MySQL.

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

Required Switches:

* :doc:`fwflag_d`, :doc:`fwflag_c`, :doc:`fwflag_t`
* :doc:`fwflag_f`
* :doc:`fwflag_outcome_table`, :doc:`fwflag_outcomes`

Optional Switches:

* :doc:`fwflag_folds`

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

Requires MySQL — broken against SQLite, see Known issue above:

.. 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 --folds 10 --predict_cv_to_feats cv_age