I am working on a Rare event classification problem. I Have 95% of the data as a majority class and 5% of the data as the minority class. I use classification trees algorithm. I am measuring the goodness of the model using confusion matrix.
As the i have the minority class just 5% of the total data, even though my prediction performance of minority class is close to 70%, the total number of errors are high.
For example, here is my confusion matrix. 0 1 0 213812 7008 1 29083 16877
Though the Minority class(class 1) has predicted 16877 times correctly(70% and the misclassifcation is just 30%, but the absolute value of the misclassifcation is very high(29083) comparing to the correctly predicted minotriy class (16877). Which makes the solution less usable for the business.
Is there any idea on handling these kind of issues in such rare event modelling.
Kind note: I have balanced the target variable using the SMOTE algorithm before applying Classification tree.