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

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Model tuning

As you are evaluating your model, you may, at some point, determine that you need to choose a different model or introduce more features/variables/hyper-parameters to improve the efficiency and performance of your model. One good way of reducing your exposure here is to spend the extra time in the Data collection section and Data preparation section. As we said earlier, there is simply no substitute for a lot of correct data.

If you have to tune your models, and you will, there are many approaches to doing so. Here are just a few:

  • Grid search
  • Random search
  • Bayesian optimization
  • Gradient-based optimization
  • Evolutionary optimization

Let's look at an example dataset—the infamous and always used Iris dataset.

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