Run

3836

Task 2249 (Supervised Data Stream Classification) baseball
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 |

0.5414 Per class |

0.8708 Per class |

0.0943 |

1059.7941 |

0.1179 |

0.4444 |

1340 Per class |

0.8622 Per class |

0.9045 |

1.585 |

0 |

0.9045 Per class |

0.2653 |

0.4714 |

0.2446 |

0.5188 |

0.1987 |