Metrics for multi-class classification problems
In the multi-class classification field, evaluating models’ performance is as crucial as developing them. Effective evaluation hinges upon utilizing the right metrics that can accurately measure the performance of the multi-class classification models and provide insights for improvement. This section demystifies the various metrics essential for assessing the performance of multi-class classification models, providing a solid foundation for selecting and employing the right metric for your specific use case.
Confusion matrix
One of the fundamental metrics for evaluating multi-class classification models is the confusion matrix. It provides a visualization of the performance of an algorithm, typically a supervised learning (SL) one. Each row of the confusion matrix represents the instances of an actual class, while each column represents the instances of a predicted class. It’s an essential tool for understanding the...
