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A Performance Matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data. It is calculated by applying the model to a hold-out sample (the test set, created during the split step in a classification activity) taken from the build data. The values of the target are known. The known values are compared with the values predicted by the model. Performance Matrix does the following:
Measures the likelihood of the model to predict incorrect and correct values
Indicates the types of errors that the model is likely to make
The columns are predicted values and the rows are actual values. For example, if you are predicting a target with values 0 and 1, then the number in the upper right cell of the matrix indicates the false-positive predictions, that is, predictions of 1 when the actual value is 0.