ML: Score

Description

The ML: Score step scores a data table with a model trained by ML: Train Model and writes the data with one appended prediction column. Because the model arrives as an ordinary one-row table, you can train once and score many tables, or re-score whenever new data lands. The step runs as a managed PlaidCloud job (like the LLM step).

Scoring Tab

• Data Table — the table to score. It must contain every feature column the model was trained on.
• Model Table — a model table written by an ML: Train Model step. The step fails if the table does not contain exactly one row.
• Output Table — where the scored data is written.
• Prediction Column — the name of the appended column. Leave blank to use prediction. The step fails if the name already exists in the data table.

Output

The output table contains every column of the data table plus the prediction column: text class labels for classifiers, numeric values for regressors. Feature values are prepared exactly as they were at training time, and categorical values the model never saw during training are ignored rather than causing an error.

The model table must have the shape an ML: Train Model step writes — one row with the full set of model columns and a known algorithm. The model itself is loaded under a restriction that rejects anything outside the standard scientific stack, so a hand-crafted model row cannot run arbitrary code.

Limits and Caveats

• In-memory working set — the data table is read into memory, so the step targets roughly one million rows per run.
• Converted Alteryx workflows — a converted Predict tool pins the prediction column to the target variable’s name with a _predicted suffix.

Related

• ML: Train Model — train the model table this step consumes.
• Migrate Alteryx Workflows — how Alteryx Predict tools convert to this step.