9. The computerized method of claim 6 , wherein the classifier is trained for different goals including recall, precision, or F-beta measure.
6. A computerized method for identifying a website visitor, the computerized method comprising: receiving, via a first computing process performed at a server device, a set of log data for a website from a web server device, the set of log data including cookies and an identification of a website visitor corresponding to each cookie; computing, via a second computing process performed at the server device, a cookie representation for each cookie under a numeric vector space, the cookie representation being computed using cookie statistics including a frequency of hits in the log data; evaluating, via a third computing process preformed at the server device, cookie-stitching rules from a set of cookie-stitching rules by applying each cookie-stitching rule from the set of cookie-stitching rules to the cookie representations to determine an effectiveness score for each cookie-stitching rule; training, via a fourth computing process performed at the server device, a classifier of effectiveness scores for each cookie-stitching rule of the set of cookie-stitching rules, each effectiveness score being a measurement of the effectiveness of each cookie-stitching rule at identifying website visitors based on the cookie representation and comprising a binary-classification evaluation metric; and utilizing, via a fifth computing process performed at the server device, the classifier to select a most effective cookie-stitching rule for log data corresponding to an unknown website visitor, the most effective cookie-stitching rule being a cookie-stitching rule with the highest cookie-stitching effectiveness score relative to other cookie-stitching rules from the set of cookie-stitching rules, wherein the highest effectiveness score comprises a highest binary-classification evaluation metric.