Making Logistic Regression A Core Data Mining Tool Paul Komarek and Andrew Moore   View Paper (PDF)   Return to Session 19: Tools and Algorithms Abstract Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiers are accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these requirements. A combination of regularization, truncated Newton methods, and iteratively re-weighted least squares make it faster and more accurate than modern SVM implementations, and relatively insensitive to parameters. It is robust to linear dependencies and some scaling problems, making most data preprocessing unnecessary. ©2006 Association for Computing Machinery