The most appropriate metric for evaluating a model used for a highly imbalanced dataset would be C) ROC-AUC.

Here's why:

A) Accuracy is not a good metric for imbalanced datasets because it can be misleading. Models that predict the majority class will have a high accuracy but may fail to capture the minority class, which is often the point of interest in imbalanced datasets.

B) Mean Squared Error (MSE) and D) Root Mean Squared Error (RMSE) are typically used for regression problems, not classification problems.

C) ROC-AUC (Receiver Operating Characteristic - Area Under Curve) is a good metric for imbalanced datasets. It measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). It provides an aggregate measure of performance across all possible classification thresholds, and it doesn't bias towards either the majority or minority class.

So, the answer is C) ROC-AUC.

Question

The most appropriate metric for evaluating a model used for a highly imbalanced dataset would be C) ROC-AUC.

Here's why:

A) Accuracy is not a good metric for imbalanced datasets because it can be misleading. Models that predict the majority class will have a high accuracy but may fail to capture the minority class, which is often the point of interest in imbalanced datasets.

B) Mean Squared Error (MSE) and D) Root Mean Squared Error (RMSE) are typically used for regression problems, not classification problems.

C) ROC-AUC (Receiver Operating Characteristic - Area Under Curve) is a good metric for imbalanced datasets. It measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). It provides an aggregate measure of performance across all possible classification thresholds, and it doesn't bias towards either the majority or minority class.

So, the answer is C) ROC-AUC.

Knowee AI · Accepted Answer

The most appropriate metric for evaluating a model used for a highly imbalanced dataset would be C) ROC-AUC.

Here's why:

A) Accuracy is not a good metric for imbalanced datasets because it can be misleading. Models that predict the majority class will have a high accuracy but may fail to capture the minority class, which is often the point of interest in imbalanced datasets.

B) Mean Squared Error (MSE) and D) Root Mean Squared Error (RMSE) are typically used for regression problems, not classification problems.

C) ROC-AUC (Receiver Operating Characteristic - Area Under Curve) is a good metric for imbalanced datasets. It measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). It provides an aggregate measure of performance across all possible classification thresholds, and it doesn't bias towards either the majority or minority class.

So, the answer is C) ROC-AUC.

Which metric would be most appropriate for evaluating a model used for a highly imbalanced dataset?*1 pointo A) Accuracyo B) Mean Squared Erroro C) ROC-AUCo D) Root Mean Squared Error

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