|
Nr
|
RIPPER
|
RIDOR
|
PART
|
|---|
|
1
|
-F 3 -N 2.0 -O 10
|
-F 3 -S 1 -N 2.0 -A
|
-R -B -M 2 -N 3
|
|
2
|
-F 3 -N 5.0 -O 10
|
-F 3 -S 1 -N 5.0 -A
|
-R -B -M 5 -N 3
|
|
3
|
-F 3 -N 10.0 -O 10
|
-F 3 -S 1 -N 10.0 -A
|
-R -B -M 10 -N 3
|
|
4
|
-F 10 -N 2.0 -O 10
|
-F 10 -S 1 -N 2.0 -A
|
-R -B -M 2 -N 10
|
|
5
|
-F 10 -N 5.0 -O 10
|
-F 10 -S 1 -N 5.0 -A
|
-R -B -M 5 -N 10
|
|
6
|
-F 10 -N 10.0 -O 10
|
-F 10 -S 1 -N 10.0 -A
|
-R -B -M 10 -N 10
|
|
7
|
-F 100 -N 2.0 -O 10
|
-F 20 -S 1 -N 2.0 -A
|
-R -B -M 2 -N 100
|
|
8
|
-F 100 -N 5.0 -O 10
|
-F 20 -S 1 -N 5.0 -A
|
-R -B -M 5 -N 100
|
|
9
|
-F 100 -N 10.0 -O 10
|
-F 20 -S 1 -N 10.0 -A
|
-R -B -M 10 -N 100
|
|
10
|
-F 3 -N 2.0 -O 100
| |
-R -M 2 -N 3
|
|
11
|
-F 3 -N 5.0 -O 100
| |
-R -M 5 -N 3
|
|
12
|
-F 3 -N 10.0 -O 100
| |
-R -M 10 -N 3
|
|
13
|
-F 10 -N 2.0 -O 100
| |
-R -M 2 -N 10
|
|
14
|
-F 10 -N 5.0 -O 100
| |
-R -M 5 -N 10
|
|
15
|
-F 10 -N 10.0 -O 100
| |
-R -M 10 -N 10
|
|
16
|
-F 100 -N 2.0 -O 100
| |
-R -M 2 -N 100
|
|
17
|
-F 100 -N 5.0 -O 100
| |
-R -M 5 -N 100
|
|
18
|
-F 100 -N 10.0 -O 100
| |
-R -M 10 -N 100
|
|
19
| | |
-B -M 2 -C 0.25
|
|
20
| | |
-B -M 2 -C 0.1
|
|
21
| | |
-B -M 5 -C 0.25
|
|
22
| | |
-B -M 5 -C 0.1
|
|
23
| | |
-B -M 10 -C 0.25
|
|
24
| | |
-B -M 10 -C 0.1
|
|
25
| | |
-M 2 -C 0.25
|
|
26
| | |
-M 2 -C 0.1
|
|
27
| | |
-M 5 -C 0.25
|
|
28
| | |
-M 5 -C 0.1
|
|
29
| | |
-M 10 -C 0.25
|
|
30
| | |
-M 10 -C 0.1
|
- RIPPER: F: number of folds for reduced error pruning; N: minimal weights of instances within a split; O: number of optimization runs
- RIDOR: F: number of folds for reduced error pruning; S: number of shuffles for randomization; A: Flag set to use the error rate of all the data to select the default class in each step. N: minimal weight of instances within a split.
- PART: C: confidence threshold for pruning; M: minimum number of instances per leaf; R: use reduced error pruning; N: number of folds for reduced error pruning; B: Use binary splits for nominal attributes
