> For the complete documentation index, see [llms.txt](https://docs.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pentaho.com/pdia-admin/optimize-the-pentaho-system/performance-tuning/pentaho-data-mining-weka-performance-tips.md). # Pentaho data mining (Weka) performance tips The most common Weka performance issue is the `OutOfMemory` exception. This is caused by using resource-intensive algorithms with large data sources. To address this, refer to: [Increase the Memory Limit in Weka](/pdia-admin/optimize-the-pentaho-system/performance-tuning/change-the-java-vm-memory-limits.md#weka) Learning algorithms convert multi-valued discrete fields to binary indicator fields, thus potentially expanding the total number of fields. This sort of pre-processing can result in two copies of the data being held in main memory briefly until the transformation is complete. So even if you have enough memory to complete the task, it could take a while to perform. For this reason, you may need to run Weka on very fast multi-core, multi-CPU 64-bit machines if you are concerned with poor performance. Beyond this, data mining tuning involves looking at each algorithm you're using and tweaking its parameters to improve the speed and accuracy of the results. This is always data- and algorithm-specific, and requires empirical experimentation. If you are running out of memory or experiencing poor performance, you might consider switching to an incrementally learning algorithm such as: * Naive Bayes * Naive Bayes multinomial * DMNBtext * AODE and AODEsr * SPegasos * SGD * IB1, IBk and KStar * Locally weighted learning * RacedIncrementalLogitBoost * Cobweb --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.pentaho.com/pdia-admin/optimize-the-pentaho-system/performance-tuning/pentaho-data-mining-weka-performance-tips.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.