 | 1 | initial version |
Getting output after compiling letter-recognition.cpp of sample code:
The database ./letter-recognition.data is loaded.
Training the classifier ..
Recognition rate: train = 86.2%, test = 82.0%
Number of trees: 100
var# importance (in %):
0 6.3
1 6.3
2 6.3
3 6.3
4 6.3
5 6.3
6 6.3 7 6.3 8 6.3 9 6.3 10 6.3 11 6.3 12 6.3 13 6.3 14 6.3 15 6.3 16 6.3
| | 2 | No.2 Revision |
Getting output after compiling letter-recognition.cpp of sample code:
The database ./letter-recognition.data is loaded.
Training the classifier ..
Recognition rate: train = 86.2%, test = 82.0%
Number of trees: 100
var# importance (in %):
0 6.3
1 6.3
2 6.3
3 6.3
4 6.3
5 6.3
6 6.3
7 6.3
....
`model->setCalculateVarImportance(true);
Mat var_importance = model->getVarImportance(); `
6.3
8 6.3
9 6.3
10 6.3
11 6.3
12 6.3
13 6.3
14 6.3
15 6.3
16 6.3
| | 3 | No.3 Revision |
Getting output after compiling letter-recognition.cpp of sample code:
The database ./letter-recognition.data is loaded.
Training the classifier ..
Recognition rate: train = 86.2%, test = 82.0%
Number of trees: 100
var# importance (in %):
0 6.3
1 6.3
2 6.3
3 6.3
4 6.3
5 6.3
6 6.3
....
`model->setCalculateVarImportance(true);
Mat var_importance = model->getVarImportance(); `
| | 4 | No.4 Revision |
Getting output after compiling letter-recognition.cpp of sample code:
The database ./letter-recognition.data is loaded.
Training the classifier ..
Recognition rate: train = 86.2%, test = 82.0%
Number of trees: 100
var# importance (in %):
0 6.3
1 6.3
2 6.3
3 6.3
4 6.3
5 6.3
6 6.3
....
`model->setCalculateVarImportance(true);
Mat var_importance = model->getVarImportance(); `
