Fraud Detection by Unsupervised Learning

This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transactions as outliers. The H2O Isolation Forest Predictor node produces two columns that can be used to identify outliers: outlier score and mean length. Here we identify outliers based on the mean length, which is the average number of random splits required to isolate a data point from the other data points. The threshold for the mean length is optimized using a parameter optimization loop.

External resources

• Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques
• Optimization Loop
• Four Techniques for Outlier Detection
• H2O Machine Learning Example Workflows
• Dataset on Kaggle