{"title":"Boosting Learner Loop End","nodeType":"LoopEnd","id":"*a36eXM94uKOrTIlo","factoryName":"org.knime.ensembles.boosting.BoostingLearnerLoopEndNodeFactory","factoryId":"org.knime.ensembles.boosting.BoostingLearnerLoopEndNodeFactory","path":["Analytics","Mining","Ensemble Learning"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>Together with the corresponding loop start node a boosting loop can be constructed. It repeatedly trains simple models and weighs them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. it can also cope with multi-class problems. The loop is stopped either after the maximum number of iterations has been reached or the weight for a model is only slightly above 0 (meaning the prediction error is too big).</p>\n        ","shortDescription":"Loop end node for learning an ensemble model with boosting.","keywords":[],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","Ensemble Learning"],"icon":"data:image/png;base64,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","views":[],"inPorts":[{"objectClass":"org.knime.core.node.port.PortObject","color":"9b9b9b","dataType":"PortObject","optional":false,"name":"Model","description":"The trained model."},{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Predicted data","description":"The data with predicted classes and also the real class values."}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Boosting model","description":"The boosted models together with their weights in data table."}],"dynInPorts":[],"dynOutPorts":[],"additionalSiteInformation":{"url":"https://update.knime.com/analytics-platform/5.12","trusted":true,"enabledByDefault":true,"name":"KNIME Analytics Platform 5.12 Update Site","version":"5.12.0"},"dialog":[{"sectionName":"Options","fields":[{"name":"Real class column","description":"The column from the second input table that contains the real class values for each row.","optional":false},{"name":"Predicted class column","description":"The column from the second input table that contains the predicted class values for each row.","optional":false},{"name":"Number of iterations","description":"The number of iterations the loop should be run, i.e., the number of models to be learned.","optional":false},{"name":"Use seed for random numbers","description":"Check this option if you want to use a fixed seed for generating random numbers. This ensures that for the same input data always the same result is produced in each iteration.","optional":false},{"name":"Seed","description":"Enter the seed for the random number generator here.","optional":false}]}],"links":[],"kudos":[],"kudosCount":0,"bundleInformation":{"bundleVersion":"5.12.0.v202605041104","bundleName":"KNIME Ensemble Learning","bundleSymbolicName":"org.knime.ensembles","bundleVendor":"KNIME AG, Zurich, Switzerland","featureSymbolicName":"org.knime.features.ensembles","featureName":"KNIME Ensemble Learning Wrappers","featureVersion":"5.12.0.v202605041104","featureVendor":"KNIME AG, Zurich, Switzerland","extensionId":"*_1Bvd90_2VpXdZt5"},"frequentSuccessors":[{"id":"*Mqb5R0RaarNo4UV1","factoryName":"org.knime.ensembles.boosting.BoostingPredictorLoopStartNodeFactory","probability":0.24242424242424243},{"id":"*SMkaasH0Wn3tKqfo","factoryName":"org.knime.base.node.mine.scorer.accuracy.AccuracyScorerNodeFactory","probability":0.18181818181818182},{"id":"*SJGWHr1skh5dpEvM","factoryName":"org.knime.ensembles.boosting.BoostingPredictorLoopEndNodeFactory","probability":0.09090909090909091},{"id":"*QUqps_kWbfHPBgyu","factoryName":"org.knime.base.node.mine.scorer.numeric.NumericScorerNodeFactory","probability":0.06060606060606061},{"id":"*AwzC_JyEuicS9pXC","factoryName":"org.knime.base.node.meta.xvalidation.AggregateOutputNodeFactory","probability":0.06060606060606061},{"id":"*e3rnMYIl5BZphzl1","factoryName":"org.knime.base.node.viz.roc.ROCNodeFactory","probability":0.030303030303030304},{"id":"*Ea5mI3GFXonzfQTM","factoryName":"org.knime.base.node.mine.treeensemble2.node.randomforest.learner.regression.RandomForestRegressionLearnerNodeFactory","probability":0.030303030303030304},{"id":"*gwwIZ5c5EYsJErci","factoryName":"org.knime.base.node.preproc.sorter.SorterNodeFactory","probability":0.030303030303030304},{"id":"*io0onqmszrZBZI8q","factoryName":"org.knime.ensembles.boosting.BoostingLearnerLoopStartNodeFactory","probability":0.030303030303030304},{"id":"*JiJhJpgkWWuOfG2s","factoryName":"org.knime.ext.poi2.node.write2.XLSWriter2NodeFactory","probability":0.030303030303030304}]}