Data set about utilities fraud detection is not balanced enough i.e. imbalanced. In such a data set, accuracy score cannot be the measure of performance as it may only be predict the majority class label correctly but in this case our point of interest is to predict the minority label. But often minorities are treated as noise and ignored. So, there is a high probability of misclassification of the minority label as compared to the majority label. For evaluating the model performance in case of imbalanced data sets, we should use Sensitivity (True Positive rate) or Specificity (True Negative rate) to determine class label wise performance of the classification model. If the minority class label’s performance is not so good, we could do the following:
- We can use under sampling or over sampling to balance the data.
- We can change the prediction threshold value.
- We can assign weights to labels such that the minority class labels get larger weights.
- We could detect anomalies.