| 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 |
0.9435 Per class |
0.692 Per class |
0.6604 |
707831.9279 |
0.0661 |
0.18 |
1000000 Per class |
0.6905 Per class |
0.6943 |
3.3219 |
0 |
0.6943 Per class |
0.367 |
0.3 |
0.2162 |
0.7205 |
148.7 |