# Combine models Model / supervised bagging, boosting, stacking, voting, a choice of learners, AutoML Page: ## Status Not built yet. arcform does not do this yet. [Its place on the roadmap](https://meridian.online/roadmap#combine-models) ## Variants - [Boost a weak learner](https://meridian.online/operations/combine-models/boost-a-weak-learner): Wrap any learner and train it round after round, reweighting rows or fitting the errors of earlier rounds, and add the rounds into one model; gradient boosted trees fix the learner as a tree. - Choose a learner automatically: Pick from a choice of algorithms, or let an automated search build and compare candidate models. - Combine several learners into one model: Train several models, by bagging or in a loop, and join them by vote, averaging or a meta learner. - Other learning tasks and learner libraries: Ranking and uplift tasks, baseline and updatable models, and add-on libraries of learners. - Wrap a learner to fit a different task: Train one inner model per class, label or group, or on a transformed label or cost matrix, and combine them. ## What other tools call it Meridian's classification; an independent blind rating agreed on 86.2% of 312 rows (exact class, version 2). - Alteryx Designer: [`Assisted Modeling`](https://help.alteryx.com/current/en/designer/tools/alteryx-intelligence-suite/machine-learning/assisted-modeling-tool.html) (read 2026-09-27) - Alteryx Designer: [`Classification`](https://help.alteryx.com/current/en/designer/tools/alteryx-intelligence-suite/machine-learning/classification-tool.html) (read 2026-09-27) - Alteryx Designer: [`DataRobot Automodel`](https://help.alteryx.com/current/en/designer/tools/predictive/datarobot-automodel-tool.html) (read 2026-09-27) - Alteryx Designer: [`Fit`](https://help.alteryx.com/current/en/designer/tools/alteryx-intelligence-suite/machine-learning/fit-tool.html) (read 2026-09-27) - Alteryx Designer: [`Regression`](https://help.alteryx.com/current/en/designer/tools/alteryx-intelligence-suite/machine-learning/regression-tool.html) (read 2026-09-27) - KNIME Analytics Platform: [`Boosting Learner Loop End`](https://api.hub.knime.com/nodes/*a36eXM94uKOrTIlo?details=full) (read 2026-09-27) - KNIME Analytics Platform: [`Boosting Learner Loop Start`](https://api.hub.knime.com/nodes/*io0onqmszrZBZI8q?details=full) (read 2026-09-27) - KNIME Analytics Platform: [`KNIME Active Learning (extension)`](https://api.hub.knime.com/extensions/org.knime.features.activelearning) (read 2026-09-27) - KNIME Analytics Platform: [`KNIME H2O Driverless AI Integration (extension)`](https://api.hub.knime.com/extensions/org.knime.features.ext.h2o.dai) (read 2026-09-27) - KNIME Analytics Platform: [`KNIME H2O Machine Learning Integration (extension)`](https://api.hub.knime.com/extensions/org.knime.features.ext.h2o) (read 2026-09-27) - KNIME Analytics Platform: [`KNIME Weak Supervision (extension)`](https://api.hub.knime.com/extensions/org.knime.features.weaksupervision) (read 2026-09-27) - KNIME Analytics Platform: [`KNIME Weka Data Mining Integration (3.6) (legacy) (extension)`](https://api.hub.knime.com/extensions/org.knime.features.ext.weka_3.6) (read 2026-09-27) - KNIME Analytics Platform: [`KNIME Weka Data Mining Integration (3.7) (extension)`](https://api.hub.knime.com/extensions/org.knime.features.ext.weka_3.7) (read 2026-09-27) - KNIME Analytics Platform: [`Model Loop End`](https://api.hub.knime.com/nodes/*vRqXIQ8KOE5e5qC7?details=full) (read 2026-09-27) - KNIME Analytics Platform: [`Model Loop Start`](https://api.hub.knime.com/nodes/*cNmurETijGKL6odr?details=full) (read 2026-09-27) - KNIME Analytics Platform: [`PMML Ensemble Loop End`](https://api.hub.knime.com/nodes/*l2kFQadTscHp7Cno?details=full) (read 2026-09-27) - KNIME Analytics Platform: [`Prediction Fusion`](https://api.hub.knime.com/nodes/*cCxw7K3QMxtaFC5l?details=full) (read 2026-09-27) - KNIME Analytics Platform: [`Voting Loop End`](https://api.hub.knime.com/nodes/*sRb-IfXX56qzfQr3?details=full) (read 2026-09-27) - mlpack: [`AdaBoost`](https://www.mlpack.org/doc/user/modeling.html) (read 2026-09-27) - mlpack: [`mlpack_adaboost_train`](https://duckdb.org/community_extensions/extensions/mlpack) (read 2026-09-27) - RapidMiner Studio: [`AdaBoost`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/adaboost.html) (read 2026-09-27) - RapidMiner Studio: [`Additive Regression`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/additive_regression.html) (read 2026-09-27) - RapidMiner Studio: [`Bagging`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/bagging.html) (read 2026-09-27) - RapidMiner Studio: [`Bayesian Boosting`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/bayesian_boosting.html) (read 2026-09-27) - RapidMiner Studio: [`Classification by Regression`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/classification_by_regression.html) (read 2026-09-27) - RapidMiner Studio: [`Default Model`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/lazy/default_model.html) (read 2026-09-27) - RapidMiner Studio: [`Hierarchical Classification`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/hierarchical_multi_class_classification.html) (read 2026-09-27) - RapidMiner Studio: [`MetaCost`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/metacost.html) (read 2026-09-27) - RapidMiner Studio: [`Multi Label Modeling`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/multi_label_model_learner.html) (read 2026-09-27) - RapidMiner Studio: [`Polynomial by Binomial Classification`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/polynomial_by_binomial_classification.html) (read 2026-09-27) - RapidMiner Studio: [`Relative Regression`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/relative_regression.html) (read 2026-09-27) - RapidMiner Studio: [`Stacking`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/stacking.html) (read 2026-09-27) - RapidMiner Studio: [`Subset Modeling`](https://docs.rapidminer.com/latest/studio/operators/extensions/Operator%20Toolbox/models/meta/subset_meta_learner.html) (read 2026-09-27) - RapidMiner Studio: [`Transformed Regression`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/transformed_regression.html) (read 2026-09-27) - RapidMiner Studio: [`Update Model`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/update_model.html) (read 2026-09-27) - RapidMiner Studio: [`Vote`](https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/ensembles/vote.html) (read 2026-09-27) - Yggdrasil Decision Forests: [`CATEGORICAL_UPLIFT`](https://github.com/google/yggdrasil-decision-forests/blob/0a54b57c95ff0cf42ecf0310ace0513a4f32cb65/yggdrasil_decision_forests/model/abstract_model.proto) (read 2026-09-27) - Yggdrasil Decision Forests: [`MULTITASKER`](https://github.com/google/yggdrasil-decision-forests/blob/0a54b57c95ff0cf42ecf0310ace0513a4f32cb65/yggdrasil_decision_forests/learner/multitasker/multitasker.h) (read 2026-09-27) - Yggdrasil Decision Forests: [`NUMERICAL_UPLIFT`](https://github.com/google/yggdrasil-decision-forests/blob/0a54b57c95ff0cf42ecf0310ace0513a4f32cb65/yggdrasil_decision_forests/model/abstract_model.proto) (read 2026-09-27) - Yggdrasil Decision Forests: [`RANKING`](https://github.com/google/yggdrasil-decision-forests/blob/0a54b57c95ff0cf42ecf0310ace0513a4f32cb65/yggdrasil_decision_forests/model/abstract_model.proto) (read 2026-09-27)