Tree: machine learning against online learning¶
The machine learning classes read the whole table and the online classes read one row at a time. They are two different parts of the library; this page runs both on the same synthetic data and reports how far apart their outputs are.
A tree asks a series of yes/no questions about the features and ends in a leaf that gives the answer. DecisionTreeClassifier chooses each question with the gini impurity; the Mondrian forests draw the questions at random and mix the answers along the path of each query.
The difference, on synthetic data¶
10 seeds. abs is the largest absolute difference between the two outputs, rel the largest relative difference; median, 90th percentile and maximum over the seeds.
| class | kind | median | p90 | max | why |
|---|---|---|---|---|---|
BatchDecisionTreeClassifier |
abs | 0 | 0 | 0 | the same gini splits on the same table |
MondrianForestClassifier |
abs | 0.01 | 0.021 | 0.03 | the trees are sampled from the whole table at once, not grown row by row, so the random splits differ |
MondrianForestRegressor |
abs | 0.0167 | 0.0303 | 0.0449 | the trees are sampled from the whole table at once, not grown row by row, so the random splits differ |
Wilcoxon and A12: not computed: the machine learning and the online value are two outputs of the same formula on the same data, so the table gives their difference and not a ranking of the two.
Run it¶
The command writes benchmarks/compare/tree.json, the table above.