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2895

Task 2126 (Supervised Data Stream Classification) covertype
Uploaded 28-04-2014 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 |

0.7856 Per class |

0.6226 Per class |

0.4263 |

64304.4953 |

0.135 |

0.2449 |

110393 Per class |

0.6113 Per class |

0.6419 |

2.8074 |

0 |

0.6419 Per class |

0.5511 |

0.3499 |

0.2733 |

0.781 |

6.06 |