| Issue | #Downvotes for this reason | By |
|---|
| 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 |
0.9548 Per class |
0.9179 Per class |
0.8323 |
3840.6764 |
0.0825 |
0.5 |
4601 Per class |
0.9312 Per class |
0.917 |
1 |
0.917 Per class |
0.165 |
0.5 |
0.2858 |
0.5717 |
0.25 |