| 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 |
0.6609 Per class |
0.5925 Per class |
0.1857 |
195092.7675 |
0.4098 |
0.5 |
1000000 Per class |
0.7104 Per class |
0.663 |
1 |
0 |
0.663 Per class |
0.8196 |
0.5 |
0.4539 |
0.9079 |
40.67 |
Severity: Core Warning
Message: Module 'mysqli' already loaded
Filename: Unknown
Line Number: 0
Backtrace: