This paper proposes an automated web site evaluation approach using machine learning to extract evaluation criteria from the existing evaluation data. Evaluating web sites is a significant task because evaluated web sites provide useful information for users to estimate sites' validation and popularity. Although many practical approaches have been taken to present a measuring stick for web sites, their evaluation criteria are set up manually. Thus, we develop a method to obtain evaluation criteria automatically and rank web sites with the learned classifier. Evaluation criteria are discriminant functions learned from a set of ranking information and evaluation features collected automatically by web robots. We conducted experiments and confirmed the effectiveness of our approach and its potential in performing high quality web site evaluation.