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Mastering Predictive Analytics with R - Second Edition by Rui Miguel Forte, James D. Miller

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Summary

In this chapter, we explored the fundamental ideas surrounding predictive models. We saw that there are many ways to categorize models, learning important distinctions in the process, such as supervised versus unsupervised learning and regression versus classification. Next, we outlined the steps involved in building a predictive model, starting from the process of data collection all the way to model evaluation and deployment. Critically, this process is an iterative one, and most often we arrive at our final model after having tried out and trained several different models.

We also introduced our first model, the k-nearest neighbor model, which is useful in performing classification and regression alike. kNN is a very flexible model that ...

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