{"title":"Fuzzy Rule Predictor","nodeType":"Predictor","id":"*lTnqesML5yHhWk_g","factoryName":"org.knime.base.node.mine.bfn.fuzzy.FuzzyBasisFunctionPredictor2NodeFactory","factoryId":"org.knime.base.node.mine.bfn.fuzzy.FuzzyBasisFunctionPredictor2NodeFactory","path":["Analytics","Mining","Rule Induction","Fuzzy Rules"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>The first port contains the Fuzzy Rule Model that is applied to the test data contained in the second input port. The output data has then one additional column containing the predicted class attribute which is the best match for all rules.</p>\n        ","shortDescription":"Applies a Fuzzy Rule Model to numeric data and outputs a prediction for each test instance.","keywords":[],"deprecated":false,"streamable":true,"tags":["Analytics","Mining","Rule Induction","Fuzzy Rules","Streamable"],"icon":"data:image/png;base64,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","views":[],"inPorts":[{"objectClass":"org.knime.base.node.mine.bfn.fuzzy.FuzzyBasisFunctionPortObject","color":"1eb9dc","dataType":"Fuzzy Basis Function","optional":false,"name":"Fuzzy rule model","description":"Fuzzy Rule Model to which test data is applied."},{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Test data","description":"Test data matching the Fuzzy Rule Model structure."}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Predicted data","description":"Predicted data with one additional classification column."}],"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":"Unkown class handling","description":"Select how to handle cases where the activation degree for all classes is below a certain threshold.\n<ul><li><b>Default</b> : Use the minimum activation threshold from the learning algorithm.</li><li><b>Ignore</b> : If selected, no lower degree of class activation is set.</li><li><b>Custom threshold</b> : Instances where the activation lies below this threshold are classified as a missing (unknown) class. This is useful in cases where the feature space is not completely covered by rules.</li></ul>","optional":false},{"name":"Threshold","description":"The threshold value between 0 and 1. Instances where the activation lies below this threshold are classified as unknown.","optional":false},{"name":"Custom prediction column name","description":"Allows you to specify a customized name for the prediction column that is appended to the input table. If not checked, <tt>Prediction(target)</tt> (where target is the name of the target column of the provided regression model) is used as default.","optional":false},{"name":"Append columns with normalized class distribution","description":"If selected, a column is appended for each class instance with the normalized probability of this row being a member of this class. The probability columns will have names like: <tt>P(targetColumn&#61;value)</tt> .","optional":false},{"name":"Probability column suffix","description":"Suffix for the probability columns.","optional":false}]}],"links":[],"kudos":[],"kudosCount":0,"bundleInformation":{"bundleVersion":"5.12.0.v202606261225","bundleName":"KNIME Base Nodes","bundleSymbolicName":"org.knime.base","bundleVendor":"KNIME AG, Zurich, Switzerland","featureSymbolicName":"org.knime.features.base","featureName":"KNIME Base nodes","featureVersion":"5.12.0.v202606261225","featureVendor":"KNIME AG, Zurich, Switzerland","extensionId":"*bjR3r1yWOznPIEXS"},"frequentSuccessors":[{"id":"*SMkaasH0Wn3tKqfo","factoryName":"org.knime.base.node.mine.scorer.accuracy.AccuracyScorerNodeFactory","probability":0.3258426966292135},{"id":"*AwzC_JyEuicS9pXC","factoryName":"org.knime.base.node.meta.xvalidation.AggregateOutputNodeFactory","probability":0.14606741573033707},{"id":"*DOkyMaii62U05xZ1","factoryName":"org.knime.base.node.preproc.filter.column.DataColumnSpecFilterNodeFactory","probability":0.033707865168539325},{"id":"*6jI6GPZYQlWirx9J","factoryName":"org.knime.base.node.viz.table.TableNodeFactory","probability":0.028089887640449437},{"id":"*e3rnMYIl5BZphzl1","factoryName":"org.knime.base.node.viz.roc.ROCNodeFactory","probability":0.02247191011235955},{"id":"*QUqps_kWbfHPBgyu","factoryName":"org.knime.base.node.mine.scorer.numeric.NumericScorerNodeFactory","probability":0.02247191011235955},{"id":"*qSAVjsLn8P2dtSZs","factoryName":"org.knime.base.node.mine.sota.SotaLearnerNodeFactory","probability":0.02247191011235955},{"id":"*PHWv6FsABEjrSc76","factoryName":"org.knime.base.node.preproc.rename.RenameNodeFactory","probability":0.016853932584269662},{"id":"*7N4LxTHoPJrHN5lT","factoryName":"org.knime.base.node.stats.viz.extended.ExtendedStatisticsNodeFactory","probability":0.016853932584269662},{"id":"*sJQ73svNoG0qOOOP","factoryName":"org.knime.base.node.viz.plotter.line.LinePlotterNodeFactory","probability":0.016853932584269662}]}