Name
Abstract | ||
|---|---|---|
Pruning is a common technique to avoid over tting in decision trees. Most pruning techniques do not ac-count for one important factor | multiple compar-isons. Multiple comparisons occur when an induction algorithm examines several candidate models and se-lects the one that best accords with the data. Mak-ing multiple comparisons produces incorrect inferences about model accuracy. We examine a method that ad-justs for multiple comparisons when pruning decision trees { Bonferroni pruning. In experiments with ar-ti cial and realistic datasets, Bonferroni pruning pro-duces smaller trees that are at least as accurate as |
Year | Venue | Keywords |
|---|---|---|
1997 | KDD | multiple comparisons,decision tree |
Field | DocType | Citations |
Decision tree,Data mining,Bonferroni correction,Computer science,Principal variation search,Multiple comparisons problem,Pruning (decision trees),Artificial intelligence,Overfitting,Machine learning,Pruning | Conference | 12 |
PageRank | References | Authors |
2.11 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Jensen | 1 | 2648 | 213.30 |
| Matthew D. Schmill | 2 | 98 | 14.67 |
| bonferroni pruning | 3 | 12 | 2.11 |