| |
k-NN
|
DT
|
SVM
|
SVR
|
|---|
| |
cv
|
test
|
cv
|
test
|
cv
|
test
|
cv
|
test
|
|
S
O
|
0.795
|
0.794
|
0.748
|
0.762
|
0.829
|
0.832
|
0.825
|
0.828
|
|
S
O
*
|
0.793
|
0.794
|
0.751
|
0.756
|
0.829
|
0.829
|
0.824
|
0.827
|
|
T
O
|
0.804
|
0.803
|
0.762
|
0.769
|
0.821
|
0.824
|
0.813
|
0.818
|
|
T
O
*
|
0.806
|
0.799
|
0.770
|
0.780
|
0.826
|
0.829
|
0.818
|
0.824
|
|
S
T
|
0.797
|
0.797
|
0.758
|
0.766
|
0.829
|
0.831
|
0.825
|
0.828
|
|
S
T
*
|
0.798
|
0.797
|
0.766
|
0.782
|
0.829
|
0.830
|
0.825
|
0.828
|
- The accuracy of each base-learner trained with original data and with extra features added in SO/SO*, TO/TO* or ST/ST*. The values reported for each classifier and regressor are respectively the validation and test accuracies of the original representation and the new representation.
