.. _auto_ml_learn_mop:

*OPTIMAL PROGNOSIS (PHP-ONLY)*
==============================

Trains all PHP-side candidate surrogates (polynomial orders 1–3 and Kriging) on a numeric dataset, scores each with K-fold cross-validation, and returns a leaderboard ranked by Coefficient of Prognosis (out-of-fold R²). Use this worker for fast, local surrogate selection on small-to-medium datasets without requiring cloud training resources.

When to use
-----------

Tagged: ``cop``, ``cross-validation``, ``kriging``, ``local``, ``lucy_ml``, ``mop``, ``php``, ``polynomial``.

Inputs
------

.. list-table::
   :header-rows: 1
   :widths: 20 20 20 20 20 20

   * - Label
     - ID
     - Type
     - Default
     - Required
     - Description
   * - Dataset
     - dataset
     - dataset
     - —
     - ✓
     - Tabular training dataset (rows of associative arrays) from which features and the target column are drawn; must contain at least 4 rows.
   * - Input Features
     - independents
     - select
     - —
     - ✓
     - One or more numeric column names from the dataset to use as input features (predictors) for all candidate surrogates.
   * - Target Column
     - clabel
     - select
     - —
     - ✓
     - Name of the single numeric column in the dataset to be used as the regression target (response variable).
   * - Cross-Validation Folds
     - kfolds
     - text
     - 5
     - 
     - Number of folds for K-fold cross-validation (integer); defaults to 5 — increase for larger datasets to obtain tighter Coefficient of Prognosis estimates.
   * - Shuffle Seed
     - seed
     - text
     - —
     - 
     - Optional integer seed for the fold-shuffle RNG; leave blank for a non-deterministic run or set to any integer for fully reproducible CV splits.
   * - Candidate Surrogates
     - surrogates
     - select
     - *(complex)*
     - 
     - Subset of candidate surrogate types to compete (polynomial_o1, polynomial_o2, polynomial_o3, kriging_gaussian); defaults to all four — narrow the list to skip slow models on very small datasets.

Outputs
-------

.. list-table::
   :header-rows: 1
   :widths: 20 20 20 20

   * - Label
     - ID
     - Type
     - Description
   * - Leaderboard
     - leaderboard
     - dataset
     - Dataset (mop_leaderboard) with one row per candidate surrogate, containing CoP, RMSE, MAE, in-sample R², elapsed time, and status, sorted by CoP descending with failed runs at the bottom.
   * - Winning Surrogate
     - winner
     - text
     - String identifier of the surrogate with the highest Coefficient of Prognosis (e.g. 'kriging_gaussian' or 'polynomial_o2'); empty string if all candidates failed.
   * - Winner Coefficient of Prognosis
     - winner_cop
     - scalar
     - Scalar Coefficient of Prognosis (out-of-fold R², range 0–1) for the winning surrogate; null if no surrogate succeeded.
   * - Winner Predictions (out-of-fold)
     - winner_predictions
     - dataset
     - Dataset (mop_predictions) of out-of-fold predictions for the winning surrogate, with columns row_index, actual, predicted, and residual — useful for residual-analysis plots.
   * - Prognosis Summary
     - mop_summary
     - json
     - JSON blob (mop_summary) containing the full run configuration (inputs, target, n_rows, kfolds, seed, candidate list) and per-surrogate fold-level scores, suitable for audit trails or downstream reporting.

Disciplines
-----------

- ai_ml.model_selection
- ai_ml.prognosis
- ai_ml.supervised.regression
- ai_ml.surrogate

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   Auto-generated from <code>platform</code> schema. Worker id: <code>ml_learn_mop</code>. Schema hash: <code>06a97cbb24fb</code>. Hand-curated docs in <code>workerexamples/</code> override this page when present.
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