Scorer

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's rows and the values from the second column by the confusion matrix'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 accuracy statistics such as True-Positives, False-Positives, True-Negatives, False-Negatives, Recall, Precision, Sensitivity, Specificity, F-measure, as well as the overall accuracy and Cohen's kappa.

Options

First column

The first column represents the real classes of the data.

Second column

The second column represents the predicted classes of the data.

Sorting strategy

Whether to sort the labels according to their appearance, or use the lexical/numeric ordering.

Reverse order

Reverse the order of the elements.

Use name prefix

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.

Missing Values

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.

Views

Confusion Matrix

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.

Workflows

• Credit Scoring Model(diksha kumari)KNIME Hub
• 00_Guided_Labeling_for_Document_Classification_(legacy)KNIME Hub
• 00_Keras_Transfer_LearningKNIME Hub
• 00_Keras_Transfer_LearningKNIME Hub
• 01_AnalyticsKNIME Hub
• Show all 474 workflows

Links

• Any advice to improve the performance of a classification model ?by mlauber71 on 2018-12-06

  forum.knime.com/p/51062
• H2O Binomial Scorer - Specifictyby mlauber71 on 2019-04-01

  forum.knime.com/p/57833
• Confusion Matrix and Confidence Levelby armingrudd on 2019-01-28

  forum.knime.com/p/53690
• Training Scoreby mlauber71 on 2019-04-06

  forum.knime.com/p/58174

Developers