RealApp#

`RealApp`(models[, X_train_orig, y_train, ...])	Maintains all information about a Pyreal application to generate explanations
`RealApp.add_model`(model[, model_id])	Add a model
`RealApp.set_active_model_id`(active_model_id)	Set a new active model
`RealApp.get_active_model`()	Return the active model
`RealApp.predict`(x[, model_id, as_dict, format])	Predict on x using the active model or model specified by model_id
`RealApp.predict_proba`(x[, model_id, ...])	Return the predicted probabilities of x using the active model or model specified by model_id, only if the model has a predict_proba method
`RealApp.prepare_feature_contributions`([...])	Initialize and fit a local feature contribution explainer
`RealApp.produce_feature_contributions`(x_orig)	Produce a feature contribution explanation
`RealApp.produce_narrative_feature_contributions`(x_orig)	Produce a feature contribution explanation, formatted in natural language sentence format using LLMs.
`RealApp.train_feature_contribution_llm`([...])	Run the training process for the LLM model used to generate narrative feature contribution explanations.
`RealApp.prepare_feature_importance`([...])	Initialize and fit a global feature importance explainer
`RealApp.produce_feature_importance`([...])	Produce a GlobalFeatureImportance explainer
`RealApp.prepare_similar_examples`([...])	Initialize and fit a nearest neighbor explainer
`RealApp.produce_similar_examples`(x_orig[, ...])	Produce a SimilarExamples explainer
`RealApp.from_sklearn`([pipeline, model, ...])	Create a RealApp from a sklearn pipeline or model and transformers. Must provide one of: - just pipeline - just model - model and transformers.