How would you measure model effectiveness without using any of confusion matrix metrics given the data is highly imbalanced

Question

Accepted Answer

One way to measure model effectiveness without using confusion matrix metrics is by using area under the receiver operating characteristic curve (AUC-ROC).

Calculate the AUC-ROC score to evaluate the model's ability to distinguish between positive and negative classes.
AUC-ROC considers the entire range of classification thresholds and is insensitive to class imbalance.
Higher AUC-ROC score indicates better model performance.
Example: A model with an AUC-ROC score of 0.85 performs better than a model with a score of 0.70.

Accepted Answer

We can use gini co-efficient for this

ie 2*ROC-1

MORE HIGHER THE VALUE MORE UNSTABLE THE MODEL IS

How would you measure model effectiveness without using any of confusion matrix metrics given the data is highly imbalanced

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