ML PREDICT¶
Applies a previously trained ML model to a new dataset to generate predictions, supporting both scalar and curve-valued targets. It accepts a saved model file (.pkl, .csv manifest, or a registered math-model ID) and returns a dataset of predicted values aligned to the input rows. Use this worker whenever you need to score new data points with a model produced by the ML Learn worker.
When to use¶
Tagged: curve prediction, inference, math model, ml, per-target model, pkl, predict, scoring.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Prediction Dataset | dataset | dataset | — | ✓ | Tabular dataset whose rows are the samples to be scored; each column is a model input feature. Must be non-empty; rows are matched positionally to predictions in the output. |
| Saved Model File | mfile | text | — | ✓ | Path, d3VIEW math-model ID, or comma-separated list of .pkl file names pointing to the serialised model(s) to use for inference; may also be a .csv manifest produced by the ML Predict-Info worker listing per-target best models. |
| Learn Dataset | reference_dataset | dataset | — | Optional training (learn) dataset used as a reference when reconstructing curve predictions; leave empty for scalar-target models. | |
| Raw Curve Column Name | raw_curve_column | text | — | Column name in dataset whose x-values are used to rebuild the predicted time/frequency curve; required only for curve-valued targets, ignored otherwise. | |
| Inputs | inputs | text | — | Comma-separated list of input feature column names; when provided together with targets the worker computes prediction-accuracy metrics against the supplied ground truth. | |
| Targets | targets | text | — | Comma-separated list of target column names present in dataset; used alongside inputs and raw_vs_predictions to evaluate and report prediction accuracy. | |
| Training Raw vs Predictions | raw_vs_predictions | dataset | — | Optional dataset containing ground-truth target values aligned to dataset; used to calculate and attach accuracy statistics (e.g. R², RMSE) to the output. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| Predictions | dataset | dataset | Dataset of model predictions with one row per input sample; scalar targets appear as numeric columns and curve targets are stored as serialised CurveGroup objects keyed by target name, with original row id values preserved. |
Disciplines¶
- ai_ml.supervised.classification
- ai_ml.supervised.regression
- ai_ml.surrogate
Auto-generated from platform schema. Worker id: ml_predict. Schema hash: d229c9f9b122. Hand-curated docs in workerexamples/ override this page when present.