Evaluating Multi-level Categorization: 8 Hierarchical Classification Error Metrics

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Hierarchical or multi-level classification problems involve the classification of instances into multiple levels or layers of nested categories. Evaluating the performance of models in such scenarios requires metrics that can handle the complexity introduced by the hierarchical structure of the labels. Here are some error metrics suitable for hierarchical classification problems:

Hierarchical Classification Metrics:

1. Hierarchical Precision, Recall, and F1 Score:

• Hierarchical precision, recall, and F1 score are typically calculated using the micro, macro, or weighted averaging schemes provided by scikit-learn.
• Advantage: Extend traditional precision, recall, and F1 score to hierarchical structures.
• Consideration: Defined for each level of the hierarchy, providing insights into performance at different levels.

from sklearn.metrics import precision_score, recall_score, f1_score

# Example true labels and predicted labels for a hierarchical classification problem
true_labels = [[1, 0, 0, 1], [0, 1, 1, 0], [1, 1, 0, 0]]
predicted_labels = [[1, 0, 0, 1], [0, 1, 0, 0], [1, 1, 1, 0]]

# Flatten the true and predicted labels to…