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Objective: The objective of this assignment is to familiarize you with predictive probabilities in the context of fraud detection and demonstrate how the choice of

Objective:
The objective of this assignment is to familiarize you with predictive probabilities in the context of fraud
detection and demonstrate how the choice of cutoff threshold impacts the classification of data.
Data Overview
Below is a table containing two columns: "Transaction Type" (0 for legitimate, 1 for fraudulent) and
"Predicted Probabilities" obtained from a machine learning model. This data represents transactions
that need to be classified as either fraudulent or legitimate based on these predicted probabilities.
Transaction Type Predicted Probabilities
1[0.477,0.523]
1[0.401,0.599]
1[0.366,0.634]
0[0.246,0.754]
1[0.322,0.678]
0[0.788,0.212]
0[0.368,0.632]
1[0.333,0.667]
0[0.679,0.321]
1[0.411,0.589]
1[0.244,0.756]
0[0.569,0.431]
1[0.211,0.789]
0[0.735,0.265]
0[0.424,0.576]
1[0.078,0.922]
0[0.622,0.378]
0[0.451,0.549]
1[0.259,0.741]
0[0.522,0.478]
Step 3: Applying Different Cutoffs
Now its your turn to classify the prediction probabilities into classes 0(legitimate) and 1
(fraudulent) using the cutoff threshold 0.5 based on the approach illustrated in Step 2.
Repeat the same procedure with cutoff thresholds 0.6,0.7.
Step 4: Calculate Confusion Matrix
Calculate the confusion matrix for each of three classifications you made in previous step (based on
cutoff thresholds 0.5,0.6,0.7).
Calculate accuracy, precision, recall and f1 score for each one.
What is the effect of cutoff threshold based on above observations?
Step 5: Calculate Cost and Identify Best Classifier
Now consider the following cost function that quantifies the cost associated with making different types
of classification errors (e.g., false positives and false negatives):
Predicted Class 0 Predicted Class 1
Actual Class 00 $ 80
Actual Class 1 $ 500
Calculate the total cost for each of the classifiers and finally decide which cutoff threshold would be
the best choice?

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