# Machine Learning {% hint style="info" %} #### **Machine Learning** The machine learning use case in Pentaho Data Integration addresses credit card fraud detection, a critical business challenge where organizations typically lose about 5% of their yearly revenue to fraudulent activities\*. This hands-on lab demonstrates three powerful approaches to fraud detection: First, it teaches how to use H2O (AutoML) to automatically discover well-performing machine learning models without extensive manual tuning. Second, it covers supervised learning algorithms that can detect fraudulent behavior by learning patterns from historical fraud cases. Third, it explores unsupervised learning methods to identify new, previously unseen types of fraud activities. A key focus of this use case is handling imbalanced datasets, which is essential since fraudulent transactions are rare compared to legitimate ones. The use case provides practical techniques for properly classifying data when dealing with this common real-world challenge, ensuring that machine learning models can effectively identify fraud without being overwhelmed by the volume of normal transactions. \*Association of Certified Fraud Examiners (ACFE) "Occupational Fraud 2024: A Report to the Nations," published in March 2024 {% endhint %} {% embed url="" %} Lab Overview {% endembed %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://academy.pentaho.com/pentaho-data-integration/use-cases/machine-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.