DATASET COMPUTE PREDICTION EVALUATION

Computes regression prediction-quality metrics (MAE, MSE, RMSE, MAPE, R²) for one or more target columns in a predictions dataset. For each target column it automatically locates the corresponding _pred companion column, reconstructs actuals from _diff offsets when needed, and returns a tabular summary of the requested metrics. Use this worker to evaluate model performance after a batch-scoring step in an ML workflow.

When to use

Tagged: MAE, MAPE, MSE, R2, RMSE, batch_evaluation, model_scoring, prediction_evaluation.

Inputs

Label ID Type Default Required Description
Predictions Dataset predictions_dataset dataset Tabular dataset (d3VIEW dataset type) containing both the predicted values (columns named <target>_pred) and, optionally, actual values or difference columns (<target>_diff); must have at least one row.
Actual Value Columns actual_value_columns text One or more column names from predictions_dataset that identify the target variables to evaluate; accepts bare target names as well as <target>_pred or <target>_diff suffixed variants — suffixes are stripped automatically.
Metrics Requested metrics_requested select One or more regression metrics to compute: MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percentage Error, in %; rows where actual = 0 are skipped), or R2 (coefficient of determination); select all that are needed for the evaluation report.

Outputs

Label ID Type Description
Evaluation Results evaluation_results dataset Dataset with one row per target column and one column per requested metric, containing the scalar metric values (dimensionless or in the units of the target variable for MAE/RMSE/MSE); null is returned for any metric that cannot be computed (e.g., all-zero actuals for MAPE, or mismatched array lengths).

Disciplines

  • ai_ml.model_selection
  • ai_ml.supervised.regression
  • data.dataset.transform
  • data.statistics

Runnable example

A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_compute_prediction_evaluation


Auto-generated from platform schema. Worker id: dataset_compute_prediction_evaluation. Schema hash: 1ce7fc30d2e8. Hand-curated docs in workerexamples/ override this page when present.