ML PREDICT (ENSEMBLE)

Runs a list of trained models (e.g. one per batch from a memory-bounded batch-ensemble run) over a dataset and AVERAGES their per-target predictions (bagging). When the dataset also carries the actual target columns, reports held-out R^2 of the averaged prediction. The predict/eval end of the batch-ensemble ML pipeline.

When to use

Tagged: average, bagging, batch, ensemble, ml, predict, r2.

Inputs

Label	ID	Type	Default	Required	Description
Models	models	text	—	✓	Comma-separated list of trained model .pkl paths (one per batch). Also accepts a dataset/list of model-path rows.
Dataset	dataset	dataset	—	✓	Rows to predict (already cleaned/normalized/PCA-transformed the same way the models were trained). If the rows also contain the actual target columns, held-out R^2 is computed.
Target Columns	targets	text	—		Comma-separated target column names to score (e.g. SOC_p9). Defaults to every predicted ‘<target>_pred’ column with its suffix stripped.

Outputs

Label	ID	Type	Description
Ensembled Predictions	predictions	dataset	One row per input row with the averaged ‘<target>_pred’ values (plus an id).
Held-out R^2	r2	string	Pooled R^2 of the averaged prediction vs the actual targets (null when no actuals present).
Per-target R^2	per_target_r2	dataset	Per-target R^2 rows ({ target, r2, n }).
Models Used	models_used	integer	How many of the supplied models produced usable predictions.
Status	status	string	Human-readable summary.

Disciplines

• ai_ml.prediction

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