Run

3351

Task 2174 (Supervised Data Stream Classification) letter
Uploaded 30-04-2014 by Jan van Rijn

0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

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Issue | #Downvotes for this reason | By |
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moa.HoeffdingTree(1) | 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). |

moa.HoeffdingTree(1)_b | false |

moa.HoeffdingTree(1)_c | 1.0E-7 |

moa.HoeffdingTree(1)_d | NominalAttributeClassObserver |

moa.HoeffdingTree(1)_e | 1000000 |

moa.HoeffdingTree(1)_g | 200 |

moa.HoeffdingTree(1)_l | NBAdaptive |

moa.HoeffdingTree(1)_m | 33554432 |

moa.HoeffdingTree(1)_n | GaussianNumericAttributeClassObserver |

moa.HoeffdingTree(1)_p | false |

moa.HoeffdingTree(1)_q | 0 |

moa.HoeffdingTree(1)_r | false |

moa.HoeffdingTree(1)_s | InfoGainSplitCriterion |

moa.HoeffdingTree(1)_t | 0.05 |

moa.HoeffdingTree(1)_z | false |

0.9482 Per class |

0.6321 Per class |

0.6199 |

13572.6896 |

0.0325 |

0.074 |

20000 Per class |

0.6469 Per class |

0.6346 |

4.7004 |

0 |

0.6346 Per class |

0.4399 |

0.1923 |

0.1422 |

0.7395 |

3.43 |