When a dataset is loaded (from an arff file), the values are converted to floats:
datasets.py:348: target_dtype = int if target_categorical[0] else float
That's fine in itself, but when doing regression experiments, we need to upload both the predicted and actual (truth) value. However, when exporting the actual value back to strings (in the arff file), precision is lost.
For instance, here are the first values of the training set of task 738 (test server):
6.1, 6.3, 5.8, 6.1, 6.0, 5.9
This is what is exported to the predictions arff file:
6.0999999 6.30000019 5.80000019 6.0999999 6.19999981 6. 5.9000001
What would be the best way to fix this? Load the data as Decimals instead of floats? Round the data before writing to arff?
When a dataset is loaded (from an arff file), the values are converted to floats:
That's fine in itself, but when doing regression experiments, we need to upload both the predicted and actual (truth) value. However, when exporting the actual value back to strings (in the arff file), precision is lost.
For instance, here are the first values of the training set of task 738 (test server):
This is what is exported to the predictions arff file:
What would be the best way to fix this? Load the data as Decimals instead of floats? Round the data before writing to arff?