The heuristic approach

Earlier in this chapter, we introduced the three models that we will be using to detect fraud, now it's time to explore each of them in more detail. We're going to start with the heuristic approach.

Let's start by defining a simple heuristic model and measuring how well it does at measuring fraud rates.

Making predictions using the heuristic model

We will be making our predictions using the heuristic approach over the entire training data set in order to get an idea of how well this heuristic model does at predicting fraudulent transactions.

The following code will create a new column, Fraud_Heuristic, and in turn assigns a value of 1 in rows where the type is TRANSFER, and the amount is more than $200,000:

df['Fraud_Heuristic '] = np.where(((df['type'] == 'TRANSFER') &(df['amount'] > 200000)),1,0)

With just two lines of code, it's easy to see how such a simple metric can be easy to write, and quick to deploy.

The F1 score

One important thing we must consider is the...