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