Description Usage Arguments Value Examples

This routine performs non-parametric and quantile regression using SVMs.
The tested estimators are therefore estimating the conditional tau-quantiles
of Y given X. By default, estimators for five different tau values
are computed.
`svmQuantileRegression`

is a simple alias of `qtSVM`

.

1 2 3 4 5 |

`x` |
either a formula or the features |

`y` |
either the data or the labels corresponding to the features |

`...` |
configuration parameters, see Configuration. Can be |

`weights` |
the quantiles that should be estimated |

`clipping` |
absolute value where the estimated labels will be clipped. -1 (the default) leads to an adaptive clipping value, whereas 0 disables clipping. |

`do.select` |
if |

an object of type `svm`

. Depending on the usage this object
has also `$train_errors`

, `$select_errors`

, and `$last_result`

properties.

1 2 3 4 5 6 7 8 9 |

```
sh: 1: grep: Permission denied
sh: 1: /bin/cat: Permission denied
sh: 1: grep: Permission denied
sh: 1: /bin/cat: Permission denied
Welcome to SVM train (dim=4 size=800 decision_functions=0 cookie=1)
liquidSVM-train -r 1 -s -1 -S 3 -P 0 -f 3 5 -g 10 0.200000 5.000000 -l 10 0.001000 0.010000 -a 0 -w [ 0.05 0.1 0.5 0.9 0.95 ] -L 4 -d 1 -T 0 -GPU 0
Assigning samples to cells for task 0.
Considering cell 1 out of 1 for task 1 out of 1.
Fold 1: training set size 640, validation set size 160.
Fold 2: training set size 640, validation set size 160.
Fold 3: training set size 640, validation set size 160.
Fold 4: training set size 640, validation set size 160.
Fold 5: training set size 640, validation set size 160.
tpt: 0.01 tbt: 0.27 tnt: 0.01 vpt: 0.00 vbt: 0.09 it: 0.067 tt: 0.434 vt: 0.250 ii: 140077 ti: 213759 tu: 427518 vi: 803679 h2D: 0.094
Welcome to SVM select (dim=4 size=800 decision_functions=0 cookie=1)
liquidSVM-select -R 1 -W 1 -d 1
Considering cell 1 out of 1 for task 1 out of 1.
Fold 1: best validation error 0.0301.
Fold 2: best validation error 0.0318.
Fold 3: best validation error 0.0293.
Fold 4: best validation error 0.0316.
Fold 5: best validation error 0.0302.
Warning: The best gamma was 0 times at the lower boundary and 3 times at the
upper boundary of your gamma grid. 5 times a gamma value was selected.
Warning: The best lambda was 0 times at the lower boundary and 2 times at the
upper boundary of your lambda grid. 5 times a lambda value was selected.
Welcome to SVM select (dim=4 size=800 decision_functions=5 cookie=1)
liquidSVM-select -R 1 -W 2 -d 1
Considering cell 1 out of 1 for task 1 out of 1.
Fold 1: best validation error 0.0557.
Fold 2: best validation error 0.0598.
Fold 3: best validation error 0.0528.
Fold 4: best validation error 0.0561.
Fold 5: best validation error 0.0550.
Warning: The best gamma was 0 times at the lower boundary and 8 times at the
upper boundary of your gamma grid. 5 times a gamma value was selected.
Warning: The best lambda was 0 times at the lower boundary and 2 times at the
upper boundary of your lambda grid. 5 times a lambda value was selected.
Welcome to SVM select (dim=4 size=800 decision_functions=10 cookie=1)
liquidSVM-select -R 1 -W 3 -d 1
Considering cell 1 out of 1 for task 1 out of 1.
Fold 1: best validation error 0.1321.
Fold 2: best validation error 0.1313.
Fold 3: best validation error 0.1174.
Fold 4: best validation error 0.1524.
Fold 5: best validation error 0.1284.
Warning: The best gamma was 0 times at the lower boundary and 13 times at the
upper boundary of your gamma grid. 5 times a gamma value was selected.
Warning: The best lambda was 0 times at the lower boundary and 2 times at the
upper boundary of your lambda grid. 5 times a lambda value was selected.
Welcome to SVM select (dim=4 size=800 decision_functions=15 cookie=1)
liquidSVM-select -R 1 -W 4 -d 1
Considering cell 1 out of 1 for task 1 out of 1.
Fold 1: best validation error 0.0656.
Fold 2: best validation error 0.0689.
Fold 3: best validation error 0.0616.
Fold 4: best validation error 0.0872.
Fold 5: best validation error 0.0649.
Warning: The best gamma was 0 times at the lower boundary and 18 times at the
upper boundary of your gamma grid. 5 times a gamma value was selected.
Warning: The best lambda was 0 times at the lower boundary and 2 times at the
upper boundary of your lambda grid. 5 times a lambda value was selected.
Welcome to SVM select (dim=4 size=800 decision_functions=20 cookie=1)
liquidSVM-select -R 1 -W 5 -d 1
Considering cell 1 out of 1 for task 1 out of 1.
Fold 1: best validation error 0.0375.
Fold 2: best validation error 0.0440.
Fold 3: best validation error 0.0369.
Fold 4: best validation error 0.0531.
Fold 5: best validation error 0.0391.
Warning: The best gamma was 0 times at the lower boundary and 23 times at the
upper boundary of your gamma grid. 5 times a gamma value was selected.
Warning: The best lambda was 0 times at the lower boundary and 2 times at the
upper boundary of your lambda grid. 5 times a lambda value was selected.
Warning messages:
1: In selectSVMs(model, weight_number = i) :
Solution may not be optimal: try training again using max_gamma=25
2: In selectSVMs(model, weight_number = i) :
Solution may not be optimal: try training again using max_gamma=25
3: In selectSVMs(model, weight_number = i) :
Solution may not be optimal: try training again using max_gamma=25
4: In selectSVMs(model, weight_number = i) :
Solution may not be optimal: try training again using max_gamma=25
5: In selectSVMs(model, weight_number = i) :
Solution may not be optimal: try training again using max_gamma=25
Welcome to SVM test (using SVM with dim=4 trained on size=800 decision_functions=25 cookie=1)
liquidSVM-test -L 4 -v 1 1 -d 1 -T 0 -GPU 0
Task 1: Test error 0.0307.
Task 2: Test error 0.0569.
Task 3: Test error 0.1362.
Task 4: Test error 0.0591.
Task 5: Test error 0.0368.
[1] 0.05689601
[1] 0.05689601
```

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