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Hands-On Machine Learning with C# by Matt R. Cole

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Model selection and training

When creating and training a model, here are a few things that you need to consider.

  • You need to choose the appropriate machine learning algorithm for the task at hand, which will be representative of the data you are working with. You will then split this into 2-3 subsets of data: training, validation, and test. The rules for the correct proportions vary based upon the amount of data you are working with. For example, if you have 10,000 rows of data, then perhaps 20% to training and 80% to test is good. But if you have 108 rows of data, perhaps 5% training and 95% test is better.
  • There is one rule that you must always follow to the letter. Whatever fractionality you decide to use for your test, train and validation ...

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