Confusion Matrix

Confusion matrix contains information about actual and predicted classification performed by an algorithm, which enables you to visualize the accuracy. You can view the chart by selecting the output method Classification and Trend for the CNR Tree algorithm. It is an n*n matrix (where n is the number of distinct values present in the dependent column selected for the algorithm), mapping the number of occurrences for each predicted value against the actual value. The entries on the diagonal of the matrix represents the correct prediction. The entries off the diagonal of the matrix represents the misclassification.

When you hover over a class, the true predicted value and the actual count of the dataset are displayed. The derivatives table represents the efficiency (sensitivity, specificity, precision, negative prediction) of the algorithm. Using the Settings option, you can analyze the data in number, percentage, and both formats.