{"title":"Prediction Fusion","nodeType":"Predictor","id":"*cCxw7K3QMxtaFC5l","factoryName":"org.knime.ensembles.predictionfusion.PredictionFusionNodeFactory","factoryId":"org.knime.ensembles.predictionfusion.PredictionFusionNodeFactory","path":["Analytics","Mining","Ensemble Learning"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>Fuses multiple prediction confidences into one, combined prediction, using the selected fusion method. The selected fusion method will be applied to all confidence values (resulting from multiple predictions) of the same class. After the fusion method was applied, the resulting combined prediction confidences will be normalized to add up to one, such that they can be interpreted as probabilities.\n <br />\n <br />\n Note: Missing values will be skipped.</p>\n        ","shortDescription":"Fuses multiple prediction confidences into one, combined prediction.","keywords":[],"deprecated":false,"streamable":true,"tags":["Analytics","Mining","Ensemble Learning","Streamable"],"icon":"data:image/png;base64,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","views":[],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Original predictions","description":"Table containing the predictions."}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Fused predictions","description":"Table containing the fused prediction confidences and the winning class."}],"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":"Method","description":"The fusion method.\n<ul><li><b>Maximum</b> : Maximum of all prediction confidences</li><li><b>Mean</b> : Mean of all prediction confidences</li><li><b>Median</b> : Median of all prediction confidences</li><li><b>Minimum</b> : Minimum of all prediction confidences</li></ul>","optional":false},{"name":"Classes column","description":"String column from which to extract the classes from it&#39;s domain.","optional":false},{"name":"Add classes from column domain","description":"Add classes from the domain of the selected string column.","optional":false},{"name":"Classes","description":"The classes that were predicted and for which confidences are available. In case of a tie the higher class (in the list) wins.\n<ul><li><b>Class name</b> : The name of the class.</li></ul>","optional":false},{"name":"Class confidences","description":"Each row represents the predicted confidences from one classifier. The fusion method is applied to all confidence values of the same class.\n<ul><li><b>Weight</b> : Multiplies the prediction confidences as if the prediction would have been added n times. This enables a prediction to have more influence on the outcome than another. (Affects mean and median but not minimum or maximum.)</li><li><b>Confidence columns</b> : Select the confidence column for each class. The columns must be compatible with double values.\n  <ul><li><b>Column</b> : Select the confidence column for this class.</li></ul></li></ul>","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":"*SMkaasH0Wn3tKqfo","factoryName":"org.knime.base.node.mine.scorer.accuracy.AccuracyScorerNodeFactory","probability":0.4507042253521127},{"id":"*DOkyMaii62U05xZ1","factoryName":"org.knime.base.node.preproc.filter.column.DataColumnSpecFilterNodeFactory","probability":0.07042253521126761},{"id":"*ZgYyOm1P9VJVBE9e","factoryName":"org.knime.base.node.preproc.joiner.Joiner2NodeFactory","probability":0.056338028169014086},{"id":"*e3rnMYIl5BZphzl1","factoryName":"org.knime.base.node.viz.roc.ROCNodeFactory","probability":0.056338028169014086},{"id":"*AwzC_JyEuicS9pXC","factoryName":"org.knime.base.node.meta.xvalidation.AggregateOutputNodeFactory","probability":0.056338028169014086},{"id":"*OQlWyEt6N59f0jmF","factoryName":"org.knime.base.node.viz.property.color.ColorManager2NodeFactory","probability":0.028169014084507043},{"id":"*sJQ73svNoG0qOOOP","factoryName":"org.knime.base.node.viz.plotter.line.LinePlotterNodeFactory","probability":0.028169014084507043},{"id":"*oluxz9j9RhfJY3E0","factoryName":"org.knime.base.node.preproc.correlation.compute.CorrelationComputeNodeFactory","probability":0.014084507042253521},{"id":"*Oy714gpoifm4PiOZ","factoryName":"org.knime.base.node.io.csvwriter.CSVWriterNodeFactory","probability":0.014084507042253521},{"id":"*QKVni78elpxYcMm0","factoryName":"org.knime.js.base.node.viz.plotter.lift.LiftChartNodeFactory","probability":0.014084507042253521}]}