{"title":"Polynomial Regression Learner","nodeType":"Learner","id":"*uD7nYRKs3yCIXp3p","factoryName":"org.knime.base.node.mine.regression.polynomial.learner2.PolyRegLearnerNodeFactory2","factoryId":"org.knime.base.node.mine.regression.polynomial.learner2.PolyRegLearnerNodeFactory2","path":["Analytics","Mining","Linear/Polynomial Regression"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>This node performs polynomial regression on the input data and computes the coefficients that minimize the squared error. The user must choose one column as target (dependent variable) and a number of independent variables. By default, polynomials with degree 2 are computed, which can be changed in the dialog.</p>\n        ","shortDescription":"Learner that builds a polynomial regression model from the input data","keywords":[],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","Linear/Polynomial Regression"],"icon":"data:image/png;base64,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","views":[{"name":"Learned Coefficients","description":"Shows all learned coefficients all attributes."},{"name":"Scatter Plot","description":"Shows the data points and the regression function in one dimension."}],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Training data","description":"The input samples, which of the columns are used as independent variables can be configured in the dialog. The input must not contain missing values, you have to fix them by e.g. using the Missing Values node."}],"outPorts":[{"objectClass":"org.knime.core.node.port.pmml.PMMLPortObject","color":"1469af","dataType":"PMML","optional":false,"name":"Regression model","description":"The computed regression coefficients as a PMML model for use in the Regression Predictor."},{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Data with training error","description":"Training data classified with the learned model and the corresponding errors."},{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Coefficients and Statistics","description":"The computed regression coefficients as a table with statistics related to the training data."}],"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":"Target column (dependent variable)","description":"The column that contains the dependent &#34;target&#34; variable.","optional":false},{"name":"Maximum polynomial degree","description":"The maximum degree the polynomial regression function should have.","optional":false},{"name":"Independent variables","description":"Select the columns containing the independent variables and move them to the &#34;include&#34; list.","optional":false},{"name":"Missing values in input data","description":"Define how to handle rows with missing values in the input data.\n<ul><li><b>Fail</b> : Stops execution with an error if missing values occur in the input data.</li><li><b>Ignore</b> : Skips rows containing missing values so the regression model is built only on complete rows.</li></ul>","optional":false},{"name":"Number of data points to show in view","description":"This option can be used to change the number of data points in the node view if e.g. there are too many points. The default value is 10,000 points.","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":"*LyRcLydCVnHBRIb0","factoryName":"org.knime.base.node.mine.regression.predict3.RegressionPredictorNodeFactory2","probability":0.5214285714285715},{"id":"*wfQnXQWEeday4Pma","factoryName":"org.knime.base.node.mine.regression.predict2.RegressionPredictorNodeFactory","probability":0.22142857142857142},{"id":"*sJQ73svNoG0qOOOP","factoryName":"org.knime.base.node.viz.plotter.line.LinePlotterNodeFactory","probability":0.023214285714285715},{"id":"*QUqps_kWbfHPBgyu","factoryName":"org.knime.base.node.mine.scorer.numeric.NumericScorerNodeFactory","probability":0.0125},{"id":"*xg4kORa5IVFVfpPN","factoryName":"org.knime.base.node.viz.plotter.scatter.ScatterPlotterNodeFactory","probability":0.0125},{"id":"*NYPEQwQdVg09dFhS","factoryName":"org.knime.ensembles.pmmlpredict2.PMMLPredictor2NodeFactory","probability":0.008928571428571428},{"id":"*JiJhJpgkWWuOfG2s","factoryName":"org.knime.ext.poi2.node.write2.XLSWriter2NodeFactory","probability":0.008928571428571428},{"id":"*9iAY3thoKzyWdyEf","factoryName":"org.knime.ext.jfc.node.linechart.JfcLineChartColoredNodeFactory","probability":0.008928571428571428},{"id":"*DOkyMaii62U05xZ1","factoryName":"org.knime.base.node.preproc.filter.column.DataColumnSpecFilterNodeFactory","probability":0.008928571428571428},{"id":"*HjhjPWnI5BHBrI1H","factoryName":"org.knime.js.base.node.viz.plotter.line.LinePlotNodeFactory","probability":0.007142857142857143}]}