Run

3755

Task 2219 (Supervised Data Stream Classification) trains
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 |

0.7 Per class |

0.6703 Per class |

0.4 |

3.9618 |

0.3026 |

0.5 |

10 Per class |

0.8125 Per class |

0.7 |

1 |

0 |

0.7 Per class |

0.6052 |

0.5 |

0.5478 |

1.0956 |

0.0024 |