/** * Returns a string describing classifier * * @return a description suitable for displaying in the explorer/experimenter * gui */ public String globalInfo() { return "A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm" + " that is capable of learning from massive data streams, assuming that the" + " distribution generating examples does not change over time. Hoeffding trees" + " exploit the fact that a small sample can often be enough to choose an optimal" + " splitting attribute. This idea is supported mathematically by the Hoeffding" + " bound, which quantifies the number of observations (in our case, examples)" + " needed to estimate some statistics within a prescribed precision (in our" + " case, the goodness of an attribute).\n\nA theoretically appealing feature " + " of Hoeffding Trees not shared by otherincremental decision tree learners is that " + " it has sound guarantees of performance. Using the Hoeffding bound one can show that " + " its output is asymptotically nearly identical to that of a non-incremental learner " + " using infinitely many examples. For more information see: \n\n" + getTechnicalInformation().toString(); }
/** * Returns a string describing classifier * * @return a description suitable for displaying in the explorer/experimenter * gui */ public String globalInfo() { return "A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm" + " that is capable of learning from massive data streams, assuming that the" + " distribution generating examples does not change over time. Hoeffding trees" + " exploit the fact that a small sample can often be enough to choose an optimal" + " splitting attribute. This idea is supported mathematically by the Hoeffding" + " bound, which quantifies the number of observations (in our case, examples)" + " needed to estimate some statistics within a prescribed precision (in our" + " case, the goodness of an attribute).\n\nA theoretically appealing feature " + " of Hoeffding Trees not shared by otherincremental decision tree learners is that " + " it has sound guarantees of performance. Using the Hoeffding bound one can show that " + " its output is asymptotically nearly identical to that of a non-incremental learner " + " using infinitely many examples. For more information see: \n\n" + getTechnicalInformation().toString(); }