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At a basic level, these kinds of low-dimensional plots where you look at one or two variables at a time can help to give you a sense of what types of relationships you might expect to see, such as linear, non-linear, or periodic relationships, which can steer you toward an appropriate family of models. You wouldn't want to use a linear model to predict data ...


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Machine learning and statistics are related and they overlap (e.g. linear regression is studied by both communities), but they are not exactly the same thing, as they have a different emphasis. You can read more about this topic in section 1.4 of this book (p. 24-25). One way that statistics or statistical analysis can be used in machine learning is to ...


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I no longer really use validation that much, but rather only training and testing. Why? Because I mostly follow Ron Kohavi's (Stanford Univ) approach to CV. I have done a lot of validation but it seemed to be overkill, essentially causing me to ask why I have this very small-sampled parameter watch on the side from which I am supposed to learn from. You ...


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