Web Farms are clustered systems designed to provide high availability and high performance,web services. A web,farm is a group of replicated HTTP servers that reply web requests forwarded,by a single point of access to the service. To deal with this task the point of access executes a load balancing algorithm to distribute web request among,the group of servers. The present algorithms provides a short-term dynamic configuration for this operation, but some corrective actions (granting different session priorities or distributed WAN forwarding) cannot be achieved without a long-term estimation of the future web,load. On this paper we,propose a method,to forecast web,service work,load. Our approach also includes an innovative segmentation method,for the web,pages using EDAs (estimation of distribution algorithms) and the application of semi-na ¨ õve Bayes classifiers to predict future web,load several minutes before. All our analysis has been performed,using real data from a world-wide academic,portal. Keywords. Web farms, web load estimation, na¨ õve Bayes, EDAs