Fix HeapDumpOnOutOfMemoryError

✅ Solution

This error in XGBoost usually means the Java Virtual Machine (JVM) ran out of memory while training or predicting, often because the dataset or model is too large. Increase the JVM heap size by setting the `java_opts` parameter in your XGBoost configuration (e.g., `-Xmx4g` for 4GB) to allocate more memory to the Java process, or reduce the size of your data. Consider using distributed XGBoost on a cluster to handle larger datasets efficiently.