FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning. The objective in extreme multi-label learning is to learn a classifier that can automatically tag a datapoint with the most relevant subset of labels from an extremely large label space. FastXML is an efficient tree ensemble based extreme classifier that can scale to millions of labels. FastXML can be trained on most datasets using a desktop/small cluster and can make predictions in milliseconds per test point. Tree ensembles generally require a lot of RAM and FastXML is no exception.