Cross-Validation: Training, Validation, and Test Splits
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Adapted from an appendix of my MS thesis. Cross-Validation Training Phase Performance should not be evaluated using the same data that was used for training. Therefore, the first step is to split the data into a training set and a testing set. This should be done before starting work on the data, whether it is training an ML model or even doing simple statistics for identifying interesting features. Note that we want the output variable distribution to be approximately the same in the training…
1Key Takeaways
- Adapted from an appendix of my MS thesis.
- Cross-Validation Training Phase Performance should not be evaluated using the same data that was used for training.
- Therefore, the first step is to split the data into a training set and a testing set.
- This should be done before starting work on the data, whether it is training an ML model or even doing simple statistics for identifying interesting features.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that adapted from an appendix of my MS thesis.
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