Decision Tree algorithms remain influential in machine learning due to their simplicities and effectiveness, particularly when combined with boosting techniques. Recent advances in frameworks like LightGBM have introduced support for arrow datasets—a columnar data format optimized for performance. Polars, another framework, showcases notable performance improvements by minimizing data copying and enabling streaming for large data processing. The author is developing a Decision Tree Classifier utilizing Polars, focusing on efficient memory usage and execution time, while maintaining a minimal dependency structure.
Decision Trees, combined with boosting, still hold a prominent place in classification and regression tasks, especially when optimized with frameworks like LightGBM and Polars.
The arrow data format's columnar structure provides efficient data processing, which could significantly enhance Decision Tree implementations implemented via modern data frameworks.
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