---

name: supervised-modeling description: "Fit, predict with, score, and route supervised Orange3 classification/regression learners, models, evaluation routines, and supervised model/evaluate widgets." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

supervised-modeling

Use this sub-skill when the Orange3 task is about supervised learning: choosing a classifier or regressor, fitting a learner to an Orange.data.Table, calling a fitted model for predictions, evaluating learners with Orange.evaluation, or wiring supervised model/evaluate widgets.

Do not use it for exploratory plotting, projections, clustering, unsupervised visualization, generic file/SQL loading, or widget-framework development except where those surfaces are needed to feed or evaluate supervised models.

Fast routing

1. Classify the target. Use data.domain.has_discrete_class for classification and data.domain.has_continuous_class for regression. Most learners require exactly one target variable; if len(data.domain.class_vars) > 1, select one target before fitting.
2. Choose the learner layer.
   • Orange.classification.* for categorical targets.
   • Orange.regression.* for numeric targets.
   • Orange.modelling.* fitters such as RandomForestLearner, TreeLearner, KNNLearner, SVMLearner, GBLearner, and ConstantLearner when one object should dispatch to classification or regression based on the domain.
3. Fit and predict. learner(data) returns a model. model(data) returns predicted values; for classification use model(data, ret=model.Probs) or model(data, ret=model.ValueProbs) when probabilities are required.
4. Evaluate. Use Orange.evaluation.CrossValidation, ShuffleSplit, TestOnTestData, or TestOnTrainingData with a list of learners, then call score classes such as CA, AUC, F1, RMSE, and R2 on the Results object.
5. For widgets. Model widgets emit a Learner and, when valid training data is supplied, a fitted Model. Test and Score consumes Data/Test Data/Learner(s)/Preprocessor and emits Evaluation Results; Predictions consumes Data plus fitted Models.

Read next

• references/api-reference.md for learner/model/evaluation API contracts, return shapes, score families, fitters, and widget signal surfaces.
• references/workflows.md for compact classification, regression, dispatch, evaluation, and widget workflows.
• references/troubleshooting.md for target, sparse-data, fitting, domain-compatibility, empty-results, and memory failure modes.

Guardrails

• Validate the target before modeling. Classification learners raise errors such as Categorical class variable expected; regression learners raise Numeric target variable expected; model widgets show equivalent data errors.
• Do not ask for probabilities from regression models: Orange raises ValueError: cannot predict continuous distributions for probability returns on continuous targets.
• Treat Results.failed as first-class evidence. Orange evaluation suppresses learner exceptions by default; rerun with suppresses_exceptions=False when debugging a failed learner.
• Use the bundled references above for runtime guidance; do not rely on hidden local files or absolute paths when helping a future agent.