Intelligence Science and Big Data Engineering. Big Data and Machine Learning by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9783030362041
Publisher: Springer International Publishing

3.2 Metrics

When evaluating the performance of the classification model, the concept of the confusion matrix is usually given according to the actual value and the predicted value of the label. Each evaluation parameter can be described by a mathematical formula [12].

The confusion matrix is shown in Table 3. Every classifier is trained on the Moore dataset, then we calculate the confusion matrix for each classifier. Based on the confusion matrix of label, Accuracy, Precision, Recall, and F1 can be calculated from the table according to formula (5)–(8). Obviously, for the k classification problems, we need to calculate times. Finally, we use macro average and weighted average to get the global metrics.Table 3.Confusion matrix

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