Understanding Multi-Class Classification: Metrics and Evaluation
Accuracy, precision, and recall in multi-class classification 🔗
Multi-class classification involves assigning input data to one of several predefined categories, which adds complexity to evaluating model quality compared to binary classification. This guide explains how to calculate accuracy, precision, and recall for multi-class classification using methods like macro- and micro-averaging. Accuracy measures the proportion of correct predictions but may be misleading if class distributions are imbalanced. Precision and recall offer more nuanced insights into model performance per class. The article also introduces the Evidently Python library for automating quality checks in classification models, emphasizing the importance of selecting appropriate metrics based on the context of the data.
What is the main difference between multi-class and multi-label classification?
Multi-class classification assigns each object to a single category, while multi-label classification allows for multiple categories to be assigned to each object simultaneously.
Why is accuracy not always a reliable metric in multi-class classification?
Accuracy can be misleading because it does not account for class imbalance, potentially masking poor performance on minority classes.
How can the Evidently Python library help with model evaluation?
Evidently allows users to quickly calculate and visualize metrics like accuracy, precision, and recall for machine learning models, providing interactive reports and integration into production pipelines.