{"title":"Scorer","nodeType":"Other","id":"*5tsiYT6vDKM7YChN","factoryName":"org.knime.base.node.mine.scorer.accuracy.AccuracyScorer2NodeFactory","factoryId":"org.knime.base.node.mine.scorer.accuracy.AccuracyScorer2NodeFactory","path":["Analytics","Mining","Scoring"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"<p>Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of which attribute and their classification match. Additionally, it is possible to hilight cells of this matrix to determine the underlying rows. The dialog allows you to select two columns for comparison; the values from the first selected column are represented in the confusion matrix&#39;s rows and the values from the second column by the confusion matrix&#39;s columns. The output of the node is the confusion matrix with the number of matches in each cell. Additionally, the second out-port reports a number of <a href=\"https://en.wikipedia.org/wiki/Confusion_matrix\" rel=\"ugc nofollow noopener noreferrer\"> accuracy statistics</a> such as True-Positives, False-Positives, True-Negatives, False-Negatives, Recall, Precision, Sensitivity, Specificity, F-measure, as well as the overall accuracy and <a href=\"https://en.wikipedia.org/wiki/Cohen%27s_kappa\" rel=\"ugc nofollow noopener noreferrer\">Cohen&#39;s kappa</a> .</p>\n        ","shortDescription":"Compares two columns by their attribute value pairs.","keywords":["model comparison","cohens kappa","accuracy"],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","Scoring"],"icon":"data:image/png;base64,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","views":[{"name":"Confusion Matrix","description":"Displays the confusion matrix in a table view. It is possible to hilight cells of the matrix which propagates highlighting to the corresponding rows. Therefore, it is possible for example to identify wrong predictions."}],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Input table","description":"Table containing the columns to compare."}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Confusion matrix","description":"The confusion matrix."},{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Accuracy statistics","description":"The accuracy statistics 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":"First column","description":"The first column represents the real classes of the data.","optional":false},{"name":"Second column","description":"The second column represents the predicted classes of the data.","optional":false},{"name":"Sorting strategy","description":"Whether to sort the labels according to their appearance, or use the lexical/numeric ordering.\n<ul><li><b>Insertion order</b> : Keep the original order as they appear in the data.</li><li><b>Numeric</b> : Sort values numerically.</li><li><b>Lexical</b> : Sort values lexicographically.</li><li><b>Unsorted</b> : Do not modify the current order.</li></ul>","optional":false},{"name":"Reverse order","description":"Reverse the order of the elements.","optional":false},{"name":"Use name prefix","description":"The scores (i.e. accuracy, error rate, number of correct and wrong classification) are exported as flow variables with a hard coded name. This option allows you to define a prefix for these variable identifiers so that name conflicts are resolved.","optional":false},{"name":"Prefix for flow variables","description":"The prefix to use for flow variable names.","optional":false},{"name":"Missing values","description":"Choose how to treat missing values in either the reference or prediction column. Default is to ignore them (treat them as if the row did not exist). Alternatively, you can expect the table to not contain missing values in these two columns. If they do, the node will fail during execution.\n<ul><li><b>Ignore</b> : Ignore missing values (treat them as if the row did not exist).</li><li><b>Fail</b> : Fail execution if missing values are encountered.</li></ul>","optional":false}]}],"links":[],"kudos":[{"accountId":"account:user:fd74d1b4-da47-4c58-86e7-4fdd8c45dce0","user":"lukass","date":"2022-04-21T15:47:26+00:00"}],"kudosCount":1,"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":[]}