[ML] RegressionIT: Fix hyperparameters for regression tests and unmute the test

[valeriy42]

This PR fixes the flaky test muted in #93228 by fixing hyperparameters to the values that always work. Since the test is for alias fields and not for the training algorithm, fixing the hyperparameters is not dangerous.

Closes #93228

[elasticsearchmachine]

Pinging @elastic/ml-core (Team:ML)

[DonalEvans]

Would changing the test so that field_2 is 10 times field_1 instead of 2 times also help with making it more consistent, since it would make the difference between using the correct value and an incorrect one easier to spot?

The way we determine the prediction error also seems like it could be improved, since using the absolute value of the difference between the predicted and actual value is very forgiving for small numbers and very strict for large numbers (being allowed to be off by 3 when the predicted value is 2 is a huge margin of error compared to being allowed to be off by 3 when the predicted value is 600).

Perhaps instead of the absolute difference between predicted and actual, the relative difference could be used, since that would allow the final assertion to be that the predictions are on average within some % of the actual value, rather than being within some absolute range. Something like this perhaps (I chose 1% arbitrarily, I don't know how lenient the test actually needs to be):

int expectedValue = 2 * featureValue;
predictionErrorPercentSum += Math.abs(predictionValue - expectedValue) / expectedValue;
...
double meanPredictionErrorPercent = predictionErrorPercentSum / sourceData.getHits().getHits().length;
// Assert that the predicted values are on average within 1% of the expected values
assertThat(meanPredictionErrorPercent, lessThanOrEqualTo(0.01));