Comparing Performance Metrics for Multi-Class Classification Models
When working with multi-class classification problems, it is imperative to evaluate the performance of the model in order to optimize it for better predictions. In this article, we will discuss some of the most common performance metrics used for evaluating multi-class classification models.
Confusion Matrix
The confusion matrix is a tabular representation of the predicted labels vs actual labels. The diagonal elements of the matrix represent the number of correctly predicted instances for each class, while off-diagonal elements represent misclassifications. The sum of each row represents the actual instances of each class, while the sum of each column represents the predicted instances of each class.
Accuracy
Accuracy is one of the simplest and most commonly used metric for evaluating classification models. It represents the percentage of correctly classified instances out of the total instances. While accuracy is a good indicator of overall model performance, it might not be an appropriate metric when the classes are imbalanced.
Precision, Recall, and F1-Score
Precision, recall, and F1-score are performance metrics that provide a more detailed evaluation of the model. Precision represents the ratio of correctly predicted positive instances to the total predicted positive instances. Recall represents the ratio of correctly predicted positive instances to the actual positive instances. F1-score is the harmonic mean of precision and recall.
Macro vs. Micro Averaging
When working with multi-class classification problems with different class sizes, macro and micro averaging are common techniques for algorithm evaluation. Micro averaging computes the overall performance of the algorithm by computing the average of all TP, FP, TN, and FN of the algorithm for each class separately. Macro averaging, on the other hand, computes the average for each measure and then computes the mean value.
Conclusion
Evaluating the performance of multi-class classification models requires an understanding of the performance metrics used for evaluation. In order to optimize the model for better predictions, it is important to select the appropriate metric that suits the specific problem at hand. While accuracy is the simplest metric, more detailed metrics such as precision, recall, and F1-score can provide a better evaluation of the model.