Will parameter sweeping on one split of data followed by cross validation...

spark_sara

Let's call our dataset splits train/test/evaluate. We're in a situation where we require months of data. So we prefer to use the evaluation dataset as infrequently as possible to avoid polluting our results. Instead, we do 10 fold cross validation (CV) to estimate how well the model might generalize. We're training deep learning models that take between 24-48 hours each, and the process of parameter sweeping is obviously very slow when...

Originally asked by Philipp Cannons on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

Depending on the number of features you have you might want to try reducing features in order to reduce overfitting and speed up your model. I assume you are using proper regularization as well. PCA may be an option to help if time is the main issue, medium PCA . As the medium article states PCA can be used to reduce the dimensionality of the data while keeping retaining variance. If you run a random forest on the different sets and the feature importance ranking is at all different that is a big issue and the number of features should be reduced. Ideally, the features should be cropped till they have the same (structure/feature importance) but slightly different weights associated with...

Full answer by Michael Hearn: https://ai.stackexchange.com/a/16500 (CC BY-SA 4.0).

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