Bug while Executing SupportVectorMachineRegressor

[TellemHD]

Describe the bug

The Error Message accures because of the SupportVectorMachineRegressor:

2024-05-03 16:17:21.636 [debug] [Client Services] [Runner] [a3256b9f-e847-4661-b1f1-00df5764c1c9] Error occurred while learning: Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.
    at c:\Users\TimLocke\AppData\Roaming\Code\User\globalStorage\safe-ds.safe-ds\runnerVenv\Lib\site-packages\safeds_runner\server\_pipeline_manager.py line 216
    at <frozen runpy> line 226
    at <frozen runpy> line 98
    at <frozen runpy> line 88
    at admissionPredictBug/gen_BUg_admissionPredictPipeline line 4
    at admissionPredictBug/gen_BUg line 106 (mapped to 'BUg.sds' line 9)
    at admissionPredictBug/gen_BUg line 17 (mapped to 'BUg.sds' line 14)
    at admissionPredictBug/gen_BUg line 54 (mapped to 'BUg.sds' line 28)
    at c:\Users\TimLocke\AppData\Roaming\Code\User\globalStorage\safe-ds.safe-ds\runnerVenv\Lib\site-packages\safeds_runner\server\_pipeline_manager.py line 353
    at c:\Users\TimLocke\AppData\Roaming\Code\User\globalStorage\safe-ds.safe-ds\runnerVenv\Lib\site-packages\safeds_runner\memoization\_memoization_map.py line 177
    at c:\Users\TimLocke\AppData\Roaming\Code\User\globalStorage\safe-ds.safe-ds\runnerVenv\Lib\site-packages\safeds\ml\classical\regression\_support_vector_machine.py line 221
    at c:\Users\TimLocke\AppData\Roaming\Code\User\globalStorage\safe-ds.safe-ds\runnerVenv\Lib\site-packages\safeds\ml\classical\_util_sklearn.py line 76

To Reproduce

pipeline admissionPredictPipeline {

    val rawData = Table.fromCsvFile("data/Admission_Predict.csv")
        .removeColumns(["Serial No."])
        .shuffleRows();

    val output = fitForManyRegressors(rawData);
}

segment fitForManyRegressors(data:Table) -> (output:Table) {
  val regressor11 = SupportVectorMachineRegressor(1.0);
  val a11, val b11 = fitAndMeasureModel(data, regressor11);

  val result = Table.fromMap({
                                      "Name": ["SupportVectorMachine"],
                                      "Training Error": [b11],
                                      "Result": [a11],
                                      });
  yield output=result;
}

segment fitAndMeasureModel(data:Table, regressor:Regressor) -> (output:Float, trainingError: Float) {
  val training, val test = data.splitRows(0.8);
  val trainingTable = training.toTabularDataset("Chance of Admit ");
  val testTable = test.toTabularDataset("Chance of Admit ");
  val fittedRegressor = regressor.fit(trainingTable);

  val resultAbsolute = fittedRegressor.meanAbsoluteError(testTable);
  val resultTrainingAbsolute = fittedRegressor.meanAbsoluteError(trainingTable);

  yield output=resultAbsolute;
  yield trainingError = resultTrainingAbsolute;
}

Expected behavior

The SUpportVectorMachine should work and calculate and get fitted and...

Screenshots (optional)

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Additional Context (optional)

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