| 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 |
0.9653 Per class |
0.7754 Per class |
0.7711 |
1616.3248 |
0.0401 |
0.18 |
2000 Per class |
0.8305 Per class |
0.794 |
3.3219 |
0.794 Per class |
0.2229 |
0.3 |
0.1842 |
0.614 |
0.35 |