February 2018
Intermediate to advanced
262 pages
6h 59m
English
Content preview from Deep Learning with PyTorch
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K-fold validation with shuffling
To make things complex and robust, you can shuffle the data every time you create your holdout validation dataset. It is very helpful for solving problems where a small boost in performance could have a huge business impact. If your case is to quickly build and deploy algorithms and you are OK with compromising a few percent in performance difference, then this approach may not be worth it. It all boils down to what problem you are trying to solve, and what accuracy means to you.
There are a few other things that you may need to consider when splitting up the data, such as:
- Data representativeness
- Time sensitivity
- Data redundancy
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