# Evaluating multiclass imbalanced problem per class

For a multiclass imbalanced problem, accuracy is not a good metric to evaluate model performance. Equally, accuracy is a global metric, so nothing like accuracy per-class (doesn't make sense).

Scikit-learn provides the classification_report function so one can evaluate model's precision/recall per class, e.g:

classification_report(y_true, y_pred, target_names=target_names)
precision    recall  f1-score   support

Class:0      0.703     0.896     0.788      4491
Class:1      0.048     0.147     0.072        75
Class:2      0.368     0.503     0.425      1097
Class:3      0.937     0.850     0.892     17162
Class:4      0.529     0.177     0.265       311

accuracy                          0.832     23136
macro avg      0.517     0.515     0.488     23136
weighted avg      0.856     0.832     0.838     23136


Are there other metrics that evaluate per-class so I can evaluate my model across more metrics than precision/recall/f1? The goal is to assess the model on a per-class basis.