15. A method performed by one or more computing devices, the method comprising: receiving clickcounts of respective training URLs, each clickcount indicating a number of times that a corresponding training URL was clicked in a set of search engine results; running an implementation of a machine learning algorithm on the one or more computing devices to compute a trained model that is then stored on the one or more computing devices, and passing the clickcounts and respective URLs to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs; and building a first index and a second index of web pages by receiving a set of URLs identifying the web pages, building feature vectors for the URLs, computing probabilities of the web pages of the URLs being searched in the future by users by processing the feature vectors with the trained model, and determining whether to include individual of the URLs in either the first index or the second index based on the computed probabilities, wherein the first index and the second index comprise respective mappings between contents of the web pages and the URLs of the web pages.
15. A method performed by one or more computing devices, the method comprising: receiving clickcounts of respective training URLs, each clickcount indicating a number of times that a corresponding training URL was clicked in a set of search engine results; running an implementation of a machine learning algorithm on the one or more computing devices to compute a trained model that is then stored on the one or more computing devices, and passing the clickcounts and respective URLs to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs; and building a first index and a second index of web pages by receiving a set of URLs identifying the web pages, building feature vectors for the URLs, computing probabilities of the web pages of the URLs being searched in the future by users by processing the feature vectors with the trained model, and determining whether to include individual of the URLs in either the first index or the second index based on the computed probabilities, wherein the first index and the second index comprise respective mappings between contents of the web pages and the URLs of the web pages. 16. A method according to claim 15 , wherein the feature vectors further include value of same text feature types as the types of text features of the training URLs, and the computed probabilities are based at least in part on those values as computed for the URLs in the set of URLs.