What is the main goal of statistical content filtering?

The primary goal of statistical content filtering is to increase spam detection accuracy over time. This approach involves analyzing the patterns and characteristics of both spam and legitimate emails to develop models that can effectively distinguish between the two. By utilizing machine learning techniques and statistical methods, the filtering system learns from a vast collection of email data, continuously improving its ability to identify and filter out unwanted messages.

Over time, as the system is exposed to more data and feedback regarding its performance, it can adjust its algorithms and refine its criteria, ultimately resulting in higher accuracy in detecting spam. This adaptive nature of statistical content filtering is what makes it more effective than static filtering methods, which do not evolve based on new data or trends in spam tactics.