29 lines
1.5 KiB
Markdown
29 lines
1.5 KiB
Markdown
---
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title: Its Generalization That Counts
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---
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## Its Generalization That Counts
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The power of machine learning comes from not having to hard code or explicitly
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define the parameters that describe your training data and unseen data. This is
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the essential goal of machine learning: to generalize a learner's findings.
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To test a learner's generalizability, you'll want to have a separate test data
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set that is not used in any way in training the learner. This can be created by
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either splitting your entire training data set into a training and test set, or
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to just collect more data. If the learner were to use data found in the test
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data set, this would create a sort of bias in your learner to do better than in
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reality.
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One method to get a sense on how your learner will do on a test data set is to
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perform what is called **cross-validation**. This randomly splits up your
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training data into a given number of subsets (for example, ten subsets) and
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leaves one subset out while the learner trains on the rest. And then once the
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learner has been trained, the left out data set is used for testing. This
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training, leaving one subset out, and testing is repeated as you rotate through
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the subsets.
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#### More Information:
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- <a href='https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf' target='_blank' rel='nofollow'>A Few Useful Things to Know about Machine Learning</a>
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- <a href='https://stats.stackexchange.com/a/153058/132399' target='_blank' rel='nofollow'>"How do you use test data set after Cross-validation"</a>
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