Nonparametric Regression ( and Classification ) Statistical Machine Learning , Spring 2017
نویسنده
چکیده
• Note for i.i.d. samples (xi, yi) ∈ R × R, i = 1, . . . , n, we can always write yi = f0(xi) + i, i = 1, . . . , n, where i, i = 1, . . . , n are i.i.d. random errors, with mean zero. Therefore we can think about the sampling distribution as follows: (xi, i), i = 1, . . . , n are i.i.d. draws from some common joint distribution, where E( i) = 0, and yi, i = 1, . . . , n are generated from the above model • It is typical to assume that each i is independent of xi. This is a pretty strong assumption, and you should think about it skeptically. We too will make this assumption, for simplicity. It should be noted that a good portion of theoretical results that we cover (or at least, similar theory) also holds without this assumption
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