Figure 4: Logistic Regression Results
The coefficients of the logistic regression model are in terms of log(odd). The log(odd) in Table 4 lacks interpretation since it does not directly give the odds of an event occurring. Instead, they show how much each feature contributes to the model, meaning that they are more likely to predict whether or not an event will occur. Based on the logistic regression model, the amount involved in the transfer is the most important feature in detecting fraud. The features that positively affect fraud detection are amount, cash-out, and transfer. The features which negatively affect fraud prediction are the initial balance of the recipient before the transaction (i.e., oldbalanceDest) and the new balance of the recipient account after the transaction (i.e., newbalanceDest). However, these coefficients cannot be directly interpreted without first taking the exponential of the features. To find the odds, we must take the exponential of the coefficients. For example, if we take the exponential of-0.693, we get 0.5, which means that for every unit increase in X1 , the odds of y = 1  decrease by 0.5. Holding all other variables constant, a one-unit increase in X1  leads to a 50% decrease in the odds of y = 1 .
Table 4: Odds of Logistic Regression