In some cases, sampling is not compatible with dataset splitting.
For the moment, sampling is only used on very large datasets ( > 8192 rows) to speed up the training of AR-like models near the end of the training process.
Sampling is enabled by default and can be disabled (Options.mActivateSampling = False).
This can be problematic when some advanced features are used : cross-validation, time hierarchies (#163), etc.
Ensure that the dataset is sampled before the training process starts and that only the last 8192 are used.
antoinecarme
commented
3 years ago
In some cases, sampling is not compatible with dataset splitting.
For the moment, sampling is only used on very large datasets ( > 8192 rows) to speed up the training of AR-like models near the end of the training process.
Sampling is enabled by default and can be disabled (Options.mActivateSampling = False).
This can be problematic when some advanced features are used : cross-validation, time hierarchies (#163), etc.
Ensure that the dataset is sampled before the training process starts and that only the last 8192 are used.